SAP HANA 2.0 Certification Guide: Application Associate Exam

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Rudi de Louw

SAP HANA® 2.0 Certification Guide: Application Associate Exam

Dear Reader,

It has been my privilege to work with Rudi for two years now on two different editions of this book. He’s everything an editor looks for in an author: knowledgeable, dedicated, and communicative (I always know exactly what he’s working on)! SAP HANA is a difficult topic to write a certification guide for, as it’s an ever-moving target, and ensuring the most up-to-date coverage requires mastery of many areas. Rudi has always been happy to do the hard work of getting the best information, and we’ve been glad to have him.

Sadly, this will be Rudi’s final edition as author of this book, as his career moves on to new (and hopefully bigger and better!) things. Rudi, we wish you nothing but luck in all your future endeavors! (And if you happen to find yourself working on a different SAP topic where we don’t yet have a book? Well, I think I can safely say we’d be delighted to bring you back into the fold.)

What did you think about SAP HANA 2.0 Certification Guide: Application Associate Exam? Your comments and suggestions are the most useful tools to help us make our books the best they can be. Please feel free to contact me and share any praise or criti-cism you may have.

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Meagan White Editor, SAP PRESS

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© 2019 by Rheinwerk Publishing, Inc., Boston (MA) 3rd edition 2019

The Library of Congress has cataloged the printed edition as follows:Names: De Louw, Rudi, author.Title: SAP HANA 2.0 certification guide : application associate exam / Rudi de Louw.Other titles: SAP HANA certification guideDescription: 3rd edition. | Bonn ; Boston : Rheinwerk Publishing, 2019. | Revised edition of: SAP HANA certification guide / Rudi de Louw. 2016. | Includes index.Identifiers: LCCN 2019017642 (print) | LCCN 2019018195 (ebook) | ISBN 9781493218301 | ISBN 9781493218295 (alk. paper)Subjects: LCSH: Relational databases--Examinations--Study guides. | Business enterprises--Computer networks--Examinations--Study guides. | Computer programmers--Certification. | SAP HANA (Electronic resource)--Examinations--Study guides.Classification: LCC QA76.9.D32 (ebook) | LCC QA76.9.D32 D45 2019 (print) | DDC 005.75/6076--dc23LC record available at https://lccn.loc.gov/2019017642

7

Contents

Acknowledgments .............................................................................................................................. 15

Preface .................................................................................................................................................... 17

1 SAP HANA Certification Track—Overview 25

Who This Book Is For .......................................................................................................... 26

SAP HANA Certifications .................................................................................................. 27

Associate-Level Certification ......................................................................................... 28

Specialist-Level Certification ......................................................................................... 30

SAP HANA Application Associate Certification Exam ......................................... 30

Exam Objective .................................................................................................................. 32

Exam Structure .................................................................................................................. 34

Exam Process ...................................................................................................................... 36

Summary ................................................................................................................................. 37

2 SAP HANA Training 39

SAP Education Training Courses ................................................................................... 40

Training Courses for SAP HANA Certifications ........................................................ 41

Additional SAP HANA Training Courses .................................................................... 42

Other Sources of Information ........................................................................................ 43

SAP Help ............................................................................................................................... 43

SAP HANA Home Page and SAP Community ........................................................... 46

SAP HANA Academy ......................................................................................................... 46

openSAP ............................................................................................................................... 48

Hands-On with SAP HANA ............................................................................................... 49

Where to Get an SAP HANA System ........................................................................... 50

Project Examples ............................................................................................................... 61

Where to Get Data ............................................................................................................ 63

Contents8

Exam Questions ................................................................................................................... 64

Types of Questions ........................................................................................................... 64

Elimination Technique .................................................................................................... 67

Bookmark Questions ........................................................................................................ 67

General Examination Strategies ................................................................................... 68

Summary ................................................................................................................................. 69

3 Technology, Architecture, and Deployment Scenarios 71

Objectives of This Portion of the Test ........................................................................ 72

Key Concepts Refresher .................................................................................................... 73

In-Memory Technology ................................................................................................... 73

Architecture and Approach ............................................................................................ 79

Deployment Scenarios .................................................................................................... 93

SAP HANA in the Cloud ................................................................................................... 104

SAP Analytics Cloud and Business Intelligence ....................................................... 108

Important Terminology .................................................................................................... 114

Practice Questions .............................................................................................................. 116

Practice Question Answers and Explanations ........................................................ 122

Takeaway ................................................................................................................................ 126

Summary ................................................................................................................................. 127

4 Modeling Tools, Management, and Administration 129



New Modeling Process .................................................................................................... 132

New Architecture for Modeling and Development ............................................... 138

Contents 9

Creating a Project .............................................................................................................. 145

Creating SAP HANA Design-Time Objects ................................................................ 155

Building and Managing Information Models .......................................................... 162

Refactoring .......................................................................................................................... 170


Practice Questions ............................................................................................................... 175


Takeaway ................................................................................................................................ 180

Summary ................................................................................................................................. 180

5 Information Modeling Concepts 181



Tables .................................................................................................................................... 183

Views ..................................................................................................................................... 183

Cardinality ........................................................................................................................... 186

Joins ..................................................................................................................................... 186

Core Data Services Views ............................................................................................... 197

Cube ..................................................................................................................................... 200

Calculation Views ............................................................................................................. 204

Other Modeling Artifacts ............................................................................................... 208

Semantics ............................................................................................................................ 213

Hierarchies .......................................................................................................................... 213




Takeaway ................................................................................................................................ 229

Summary ................................................................................................................................. 229

Contents10

6 Calculation Views 231



Data Sources for Calculation Views ............................................................................ 234

Calculation Views: Dimension, Cube, and Cube with Star Join ........................ 235

Working with Nodes ........................................................................................................ 248

Semantics Node ................................................................................................................. 263




Takeaway ................................................................................................................................ 278

Summary ................................................................................................................................. 278

7 Modeling Functions 279



Calculated Columns ......................................................................................................... 281

Restricted Columns .......................................................................................................... 286

Filters ..................................................................................................................................... 291

Variables .............................................................................................................................. 293

Input Parameters .............................................................................................................. 297

Session Variables ............................................................................................................... 302

Currency ............................................................................................................................... 303

Hierarchies .......................................................................................................................... 308

Hierarchy Functions ......................................................................................................... 316




Takeaway ................................................................................................................................ 336

Summary ................................................................................................................................. 336

Contents 11

8 SQL and SQLScript 337



SQL ..................................................................................................................................... 340

SQLScript .............................................................................................................................. 347

Views, Functions, and Procedures ............................................................................... 362

Catching Up with SAP HANA 2.0 .................................................................................. 365




Takeaway ................................................................................................................................ 374

Summary ................................................................................................................................. 374

9 Data Provisioning 375



Concepts .............................................................................................................................. 378

SAP Data Services .............................................................................................................. 383

SAP Landscape Transformation Replication Server ............................................... 385

SAP Replication Server ..................................................................................................... 387

SAP HANA Smart Data Access ....................................................................................... 388

SAP HANA Smart Data Integration and SAP HANA Smart Data Quality ........ 392

SAP HANA Streaming Analytics .................................................................................... 395

SQL Anywhere and Remote Data Sync ....................................................................... 397

Flat Files ................................................................................................................................ 399

Web Services (OData and REST) ................................................................................... 400

SAP Vora ............................................................................................................................... 401

SAP HANA Data Management Suite ........................................................................... 402



Contents12


Takeaway ................................................................................................................................ 410

Summary ................................................................................................................................. 410

10 Text Processing and Predictive Modeling 411



Text Processing .................................................................................................................. 414

Predictive Modeling ......................................................................................................... 432




Takeaway ................................................................................................................................ 457

Summary ................................................................................................................................. 458

11 Spatial, Graph, and Series Data Modeling 459



Spatial Modeling ............................................................................................................... 462

Graph Modeling ................................................................................................................. 480

Series Data Modeling ....................................................................................................... 498




Takeaway ................................................................................................................................ 517

Summary ................................................................................................................................. 517

Contents 13

12 Optimization of Information Models 519



Architecture and Performance ..................................................................................... 521

Redesigned and Optimized Applications .................................................................. 522

Effects of Good Performance ........................................................................................ 523

Information Modeling Techniques ............................................................................. 524

Optimization Tools ........................................................................................................... 525

Best Practices for Optimization .................................................................................... 536




Takeaway ................................................................................................................................ 548

Summary ................................................................................................................................. 548

13 Security 549



Usage and Concepts ......................................................................................................... 551

SAP HANA XSA and SAP HANA Deployment Infrastructure ............................... 554

Users ..................................................................................................................................... 559

Roles ..................................................................................................................................... 565

Privileges .............................................................................................................................. 569

Assigning Roles to Users ................................................................................................. 577

Data Security ...................................................................................................................... 578




Takeaway ................................................................................................................................ 587

Summary ................................................................................................................................. 587

Contents14

14 Virtual Data Models 589



Reporting in the Transactional Systems ................................................................... 592

SAP HANA Live .................................................................................................................... 595

SAP S/4HANA Embedded Analytics ............................................................................ 608




Takeaway ................................................................................................................................ 619

Summary ................................................................................................................................. 620

The Author ............................................................................................................................................. 621

Index ........................................................................................................................................................ 623

Service Pages ..................................................................................................................................... I Legal Notes ......................................................................................................................................... II

15

Acknowledgments

“Write the vision, and make it plain upon tables, that he may run that readeth it.”

– Habakkuk 2:2 (King James Bible)

SAP PRESS had the vision for a book on the SAP HANA certification exam, and it

was my privilege to write it and make it plain. It’s my wish that every reader of this

book runs with the knowledge and understanding gained from it.

All the writing has now even produced what I jokingly refer to as a “trilogy.” This is

the third and final edition from my side.

In my more than eight-year journey with SAP HANA, I’ve met hundreds of people,

all passionate about SAP HANA. Thank you to all of you!

Thank you to the entire team at SAP PRESS for making this book happen, and espe-

cially to the SAP PRESS editor, Meagan White, and the other editors for your input.

Thank you to all my SAP Co-Innovation Lab colleagues for your support and the

discussions we had around SAP HANA. And an extra thank you to Skhu, Phillip,

and Tshepo for all your input and encouragement.

I’m very thankful to all of you: my family, my friends, my colleagues, and everyone

I’ve worked with to build great things with SAP HANA.

To my readers: I trust that your journey will be even more amazing!

17

Preface

The SAP PRESS Certification Series is designed to provide anyone who is preparing

to take an SAP certified exam with all the review, insight, and practice they need to

pass the exam. The series is written in practical, easy-to-follow language that pro-

vides targeted content focused on what you need to know to successfully take

your exam.

This book is specifically written for those interested in using the SAP HANA 2.0

version and preparing to take the SAP Certified Application Associate – SAP HANA

(C_HANAIMP_15) exam. This book will also be helpful to those of you taking some

of the older exams, such as the SPS 14 exam (C_HANAIMP_14). The SAP Certified

Application Associate – SAP HANA exam tests the taker in the areas of data model-

ing. It focuses on core SAP HANA skills, as opposed to skills that are specific to a

particular implementation type (e.g., SAP Business Warehouse on SAP HANA or

SAP Business Suite on SAP HANA). The SAP Certified Application Associate – SAP

HANA test can be taken by anyone who signs up.

Using this book, you’ll walk away with a thorough understanding of the exam’s

structure and what to expect when taking it. You’ll receive a refresher on the key

concepts covered on the exam, and you’ll be able to test your skills via sample

practice questions and answers. The book is closely aligned with the course sylla-

bus and the exam structure, so all the information provided is relevant and appli-

cable to what you need to know to prepare. We explain the SAP products and

features using practical examples and straightforward language, so you can pre-

pare for the exam and improve your skills in your day-to-day work as an SAP

HANA data modeler. Each book in the series has been structured and designed to

highlight what you really need to know.

Preface18

Structure of This Book

Each chapter begins with a clear list of the learning objectives, as shown here:

Techniques You’ll Master

� Prepare for the exam

� Understand the general exam structure

� Access practice questions for preparation

From there, you’ll dive into the chapter and get right into the test objective cover-

age.

Throughout the book, we’ve also provided several elements that will help you

access useful information:

� Tips call out useful information about related ideas and provide practical sug-

gestions for how to use a particular function. For example:

Tip

This book contains screenshots and diagrams to guide your understanding of the many

information-modeling concepts.

� Notes provide other resources to explore or special tools or services from SAP

that will help you with the topic under discussion. For example:

Note

This certification guide covers all topics you need to successfully pass the exam. It pro-

vides sample questions similar to those found on the actual exam.

� SPS 04 notes will show you what is new in SAP HANA SPS 04. This will help you

when studying exams after C_HANAIMP_15. For example:

SPS 04

When studying for the C_HANAIMP_15 exam, you can ignore these notes when doing

your final preparation. But they will be helpful to keep you up to date and can be used to

prepare for later exams when they get published.

Each chapter that covers an exam topic is organized in a similar fashion so you can

become familiar with the structure and easily find the information you need.

Here’s an example of a typical chapter structure:

Preface 19

� Introductory bullets

The beginning of each chapter discusses the techniques you must master to be

considered proficient in the topic for the certification examination.

� Topic introduction

This section provides you with a general idea of the topic at hand to frame

future sections. It also includes objectives for the exam topic covered.

� Real-world scenario

This section shows a real-world scenario where these skills would be beneficial

to you or your company.

� Objectives

This section provides you with the necessary information to successfully pass

this portion of the test.

� Key concept refresher

This section outlines the major concepts of the chapter. It identifies the tasks

you’ll need to understand or perform properly to answer the questions on the

certification examination.

� Important terminology

Just prior to the practice examination questions, we provide a section to review

important terminology. This may be followed by definitions of various terms

from the chapter.

� Practice questions

The chapter then provides a series of practice questions related to the topic of

the chapter. The questions are structured in a similar way to the actual ques-

tions on the certification examination.

� Practice question answers and explanations

Following the practice exercise are the solutions to the practice exercise ques-

tions. As part of the answer, we discuss why an answer is considered correct or

incorrect.

� Takeaway

This section provides a takeaway or reviews the areas you should now under-

stand. This refresher section identifies the key concepts in the chapter and pro-

vides some tips related to the chapter.

� Summary

Finally, we conclude with a summary of the chapter.

Now that you have an idea of how the book is structured, the following list will

dive into the individual topics of the exam covered in each chapter:

Preface20

� Chapter 1, SAP HANA Certification Track—Overview, provides a look at the dif-

ferent certifications for SAP HANA: associate, professional, and specialist. It

then looks at the general exam objectives, structure, and scoring.

� Chapter 2, SAP HANA Training, discusses the available training and training

material for test takers, so you know where to look for answers beyond the

book. We identify the SAP Education training courses available for classroom,

virtual, and e-learning, in addition to SAP Learning Hub offerings. We then look

into additional resources, including the official SAP documentation and the

SAP HANA Academy video tutorials. We also talk about the SAP HANA, express

edition, which you can use to get practical experience.

� Chapter 3, Technology, Architecture, and Deployment Scenarios, walks through

the evolution of SAP HANA’s in-memory technology, including how it

addresses the problems of the past, such as slow disks. From there, you’re given

an overview of the persistence layer, before looking at the different deployment

options, including cloud deployments, and delving into the new SAP HANA

extended application services, advanced model (SAP HANA XSA) features.

� Chapter 4, Modeling Tools, Management, and Administration, introduces you

to what’s new in SAP HANA 2.0, the new architecture and modeling process, and

the SAP HANA modeling tools, including SAP Web IDE for SAP HANA. In addi-

tion, you’ll learn how to create new information models, tables, and data.

� Chapter 5, Information-Modeling Concepts, discusses general modeling con-

cepts such as cubes, fact tables, and the difference between attributes and mea-

sures, in addition to covering general modeling best practices.

� Chapter 6, Calculation Views, looks in depth at the three primary information

views in SAP HANA and how to build information models using calculation

views.

� Chapter 7, Modeling Functions, takes what you’ve learned one step further by

showing you how to enhance your calculation views with calculated columns,

filter expressions, and more.

� Chapter 8, SQL and SQLScript, reviews how to enhance SAP HANA models using

SQL and SQLScript to create tables, read and filter data, create calculated col-

umns, implement procedures, use user-defined functions, and more.

� Chapter 9, Data Provisioning, introduces the concepts, tools, and methods for

data provisioning in SAP HANA. Coverage runs from SAP Data Services to SAP

HANA streaming analytics.

Preface 21

� Chapter 10, Text Processing and Predictive Modeling, looks at advanced topics,

such as creating a text search index, implementing fuzzy search, performing

text analysis, and creating predictive analysis models.

� Chapter 11, Spatial, Graph, and Series Data Modeling, looks at more of the

advanced topics, such as using the key components of spatial processing, work-

ing with relationships between data objects using graphs, and understanding

series data.

� Chapter 12, Optimization of Information Models, reviews how to monitor,

investigate, and optimize data models in SAP HANA. We’ll look at how optimi-

zation affects architecture, performance, and information-modeling tech-

niques. We’ll then dive into the different tools used for optimization including

the SQL Analyzer, SAP HANA cockpit, debug query mode, and performance

analysis mode.

� Chapter 13, Security, discusses how users, roles, and privileges work together.

We’ll look at the different types of users, template roles, and privileges that are

available in the SAP HANA system.

� Chapter 14, Virtual Data Models, looks at how to adapt predelivered content to

your own business solutions using SAP HANA Live. You’ll learn about SAP

HANA Live’s different views, and its two primary tools: SAP HANA Live Browser

and SAP HANA Live extension assistant. We also look at SAP S/4HANA embed-

ded analytics, which uses similar concepts.

What’s New in the Third Edition

Readers of the first two books will be interested to find out what is new in this edi-

tion. We’re working with SAP HANA 2.0, using the same user interface as is used by

SAP Cloud Platform. While the modeling concepts are still the same as SAP HANA

1.0, the way you work with SAP HANA 2.0 has changed significantly.

SAP HANA now ensures that the development you do for on-premise systems will

also work in the cloud. This was accomplished by moving SAP HANA into the con-

tainer and microservices world using SAP HANA XSA. This change has had a huge

ripple effect on architecture, modeling tools, managing models, and security, to

give a few examples. A bigger distinction now exists between design-time and run-

time objects, which leads to updated processes for creating information models

(design-time), building these information models to create runtime objects, and,

finally, storing, managing, and securing these models.

Preface22

We now use SAP Web IDE for SAP HANA instead of SAP HANA Studio. All the

screens shown in the book, with their descriptions, were updated to reflect this

change. Chapter 4 now discusses this new modeling environment, instead of the

old SAP HANA Studio and SAP HANA Web-Based Development Workbench. The

bigger distinction between the design-time and runtime objects brought in new

tools as well, such as the Git repository, the building process (for creating runtime

objects), and new rules for managing your models.

In the sections on graphical calculation views, the following additional node types

are discussed: non-equi joins, minus, intersects, hierarchy functions, and ano-

nymization. Hierarchy functionality has expanded as well.

The largest changes are in the areas of advanced modeling. The SAP training

courses now include a three-day training course on the topics of text processing,

predictive modeling, spatial processing, graph modeling, and series data. The

exam now has more emphasis on these topics, with the marks for this area moving

up from 10% to 25%.

Tip

Even though the exam is based on SAP HANA 2.0 SPS 03, this book also addresses SAP

HANA 2.0 SPS 04.

Practice Questions

We want to give you some background on the test questions before you encounter

the first few in the chapters. Just like the exam, each question has a basic structure:

� Actual question

Read the question carefully, and be sure to consider all the words used in the

question because they can impact the answer.

� Question hint

This isn’t a formal term, but we call it a hint because it tells you how many

answers are correct. If only one is correct, normally it tells you to choose the cor-

rect answer. If more than one is correct, like the actual certification examina-

tion, it indicates the correct number of answers.

� Answers

The answers to select from depend on the question type. The following ques-

tion types are possible:

Preface 23

– Multiple response: More than one correct answer is possible.

– Multiple choice: Only a single answer is correct.

– True/false: Only a single answer is correct. These types of questions aren’t

used in the exam, but they are used in the book to test your understanding.

Summary

With this certification guide, you’ll learn how to approach the content and key

concepts highlighted for each exam topic. In addition, you’ll have the opportunity

to practice with sample test questions in each chapter. After answering the prac-

tice questions, you’ll be able to review the explanation of the answer, which dis-

sects the question by explaining why the answers are correct or incorrect. The

practice questions give you insight into the types of questions you can expect,

what the questions look like, and how the answers relate to the question. Under-

standing the composition of the questions and seeing how the questions and

answers work together is just as important as understanding the content. This

book gives you the tools and understanding you need to be successful. Armed with

these skills, you’ll be well on your way to becoming an SAP Certified Application

Associate in SAP HANA.

Chapter 1

SAP HANA Certification Track—Overview


� Understand the different levels of SAP HANA certifications

� Find the correct SAP HANA certification exam for you

� Learn the scoring structure of the certification exams

� Discover how to book your SAP HANA certification exam

Chapter 1 SAP HANA Certification Track—Overview26

Welcome to the exciting world of SAP HANA! SAP as a company is moving at full

speed to update all of its solutions to use SAP HANA. Some solutions have even

changed completely to better leverage the capabilities of SAP HANA. In many

senses, the future of SAP is tied to the SAP HANA platform.

SAP HANA itself is developing at a fast pace to provide all the features required for

innovation. Many of the new areas that we see SAP working in, such as cloud and

mobile, are enabled because of SAP HANA’s many features. The best-known SAP

HANA feature is its fast response times. Both cloud and mobile can suffer from

latency issues, and they certainly improve with faster response times.

With the large role that SAP HANA now plays, the demand for experienced profes-

sionals with SAP HANA knowledge has grown. By purchasing this book, you’ve

taken the first step toward meeting these demands and advancing your career via

knowledge of SAP HANA.

Note

Five different certifications are available for SAP HANA, and each focuses on a different

topic. The focus of this book is on the SAP Certified Application Associate – SAP HANA cer-

tification, which is the most in-demand SAP HANA certification.

In this chapter, we’ll discuss the target demographic of this book before diving into

a discussion of the various SAP HANA certification exams available. We’ll then

look at the specific exam this book is based on before examining the structure of

that exam.

Who This Book Is For

This book covers SAP HANA from a modeling and application perspective for the

C_HANAIMP_15 exam. As such, this book is meant for quite a large group of people

from various backgrounds and interests. In a broad sense, this book talks about

how to work with data and information—and more people work with information

now than ever before.

This book doesn’t go into the more technical topics (e.g., installations, upgrades,

monitoring, updates, backups, and transports) addressed by some of the other SAP

HANA certifications.

SAP HANA Certifications Chapter 1 27

Let’s discuss who can benefit from this book:

� Information modelers are a core audience group for this book because the main

topic is information modeling. This includes people who come from an enter-

prise data warehouse (EDW) background.

� Developers need to create and use information models—whether for SAP appli-

cations using ABAP, cloud applications using Java or Node, web services, mobile

apps, or new SAP HANA applications using SAPUI5.

� Database administrators (DBAs) must understand how to use the new in-

memory and columnar database technologies of SAP HANA.

� Architects want to understand SAP HANA concepts, get an overview of what SAP

HANA entails, and plan new landscapes and solutions using SAP HANA.

� Data integration and data provisioning specialists need to know how to process

data in an optimal manner using SAP HANA.

� Report-writing professionals constantly create, find, and consume information

models. Often, the need for these information models arises from the reporting

needs of business users. An understanding of how to create these information

models in SAP HANA will be of great benefit.

� Data scientists, or anyone working with big data and data mining, are always

looking for faster, smarter, and more efficient ways to get results that can help

businesses innovate. SAP HANA has proven that it can certainly contribute to

these areas.

� Technical performance tuning experts have to understand the intricacies of the

new in-memory and columnar database paradigm to fully utilize and leverage

the performance that SAP HANA can provide.

If you fit into one or more of these categories, excellent! You’re in the right place.

Regardless, if you’ve bought this book in preparation for the exam, our primary

goal is to help you succeed; all exam takers are welcome.

SAP HANA Certifications

There are currently five certification examinations available for SAP HANA, as

listed in Table 1.1. Note that the certifications have different prefixes. The C prefix

indicates full, core exams for SAP HANA data modeling; and the E prefix indicates

certification exams for specialists.


Table 1.1 also lists the main SAP training courses available for each certification,

how many questions will be asked in each certification exam, and how much time

is available when taking the examination.

Although not shown here, the exams for all certifications have a unique number at

the end of their names (e.g., C_HANATEC_15 is the technical certification exam for

SAP HANA 2.0 SPS 03). We’ll discuss these different numbers later in this chapter.

We can divide these different certifications into two categories: associate and spe-

cialist (as shown in the Type of Certification column in Table 1.1). Let’s walk

through these different categories.

Associate-Level Certification

The associate-level certification proves that you understand SAP HANA core con-

cepts and can apply them in projects. Based on theory, the associate-level certifica-

tions are meant for people who are new to SAP HANA.

When setting up these certifications, we ask project members the following ques-

tion: “What would you want candidates to be able to do and understand when they

Certification Type of

Certification

Keyword SAP Training

Courses

Number of

Questions

Time in

Minutes

C_HANAIMP Associate Modeling HA100

HA300

HA301

80 180

C_HANATEC Associate Technical HA200

HA201

HA215

HA240

HA250

80 180

C_HANADEV Associate Development HA450 and

openSAP

80 180

E_HANAAW Specialist ABAP HA100

HA300

HA400

40 90

E_HANABW Specialist SAP BW BW362 40 90

Table 1.1 SAP HANA Certifications

SAP HANA Certifications Chapter 1 29

join your project immediately after passing the certification exam?” Understand-

ing the different concepts in SAP HANA and when to use them is important.

Three of the SAP HANA certifications fall under the associate level: C_HANAIMP

(modeling), C_HANATEC (technical), and C_HANADEV (development).

C_HANAIMP Modeling Certification

As previously discussed, the C prefix indicates the full, core exams for SAP HANA

data modeling. IMP refers to the implementation of SAP HANA applications. This

examination consists of 80 questions, and you’re given three hours to complete

the exam.

The certification doesn’t have any prerequisites. The SAP training courses that

help you prepare for this certification exam are HA100, HA300, and HA301. This

book and this certification exam are intended for information modelers, DBAs,

architects, SAP HANA application developers, ABAP and Java developers, people

who perform data integration and reporting, those who are interested in technical

performance tuning, SAP Business Warehouse (SAP BW) information modelers,

data scientists, and anyone who wants to understand basic SAP HANA modeling

concepts.

This certification exam tests your knowledge in SAP HANA subjects such as archi-

tecture, deployment, information modeling, security, optimization, administra-

tion, SAP HANA Live, and some advanced modeling topics, such as predictive, text,

graph, geospatial, and series data modeling.

C_HANATEC Technical Certification

The C_HANATEC certification exam is meant for technical people such as system

administrators, DBAs, SAP Basis people, technical support personnel, and hard-

ware vendors. This certification doesn’t have any prerequisites. The training

courses for this exam are HA200, HA201, HA215, HA240, and HA250.

This certification exam tests your knowledge of SAP HANA installations, updates,

upgrades, migrations, system monitoring, administration, configuration, perfor-

mance tuning, high availability, troubleshooting, backups, and security.


C_HANADEV Development Certification

The C_HANADEV certification exam is meant for SAP HANA architects and devel-

opers of both backend and frontend systems. This certification doesn’t have any

prerequisites. The main training course for this exam is HA450. The openSAP

courses by Thomas Jung and Rich Heilman will definitely help you when preparing

for this exam.

This certification exam tests your knowledge of SAP HANA development topics,

such as SAP HANA modeling; SAP HANA extended application services, advanced

model (SAP HANA XSA); SAPUI5; server-side JavaScript; SQLScript; OData services;

and deployment.

Specialist-Level Certification

The specialist exams prove that you can apply SAP HANA concepts in your current

specialist area. These are for people who are already certified in other areas outside

of SAP HANA, and want to gain additional knowledge of SAP HANA and how to

apply it to their specialties. Such people have backgrounds in SAP BW or ABAP, for

example.

Each specialist exam includes 40 questions, and you have an hour and a half to

complete the exam. They all have prerequisites (i.e., you have to be certified in a

specialist area before you’re allowed to take these SAP HANA certification exams).

There are two specialist exams:

� E_HANAAW ABAP Certification

This specialist exam for ABAP developers requires that you’re certified with

ABAP. The main additional training course for this exam is HA400, in which

you learn how ABAP development is different in SAP HANA systems than in

older SAP NetWeaver systems.

� E_HANABW SAP BW on SAP HANA Certification

This exam for people specializing in SAP BW requires that you’re certified with

SAP BW. The main training course for this exam is BW362.

SAP HANA Application Associate Certification Exam

Now, let’s focus on the objective and structure of the C_HANAIMP certification

that this book targets and look at the latest C_HANAIMP certification exam.

SAP HANA Application Associate Certification Exam Chapter 1 31

SAP is evolving fast and is constantly being updated. Originally, a new major Sup-

port Package Stack (SPS) update of SAP HANA was released twice a year, requiring

SAP Education to update all training materials and certification exams every six

months. Fortunately, SAP changed this to one release per year, so all SAP certifica-

tions remain valid for the past two versions. In other words, your SAP HANA certi-

fication, depending on when you received it, will be valid for one and a half to two

years. This is regrettably a slightly shorter time frame than for most other SAP cer-

tifications, some of which can remain valid for many years. In a sense, this is the

cost of working in such a fast-moving environment.

Table 1.2 lists the exam names for the SAP Certified Application Associate – SAP

HANA certification (C_HANAIMP) through the years.

Note

This book deals specifically with the latest C_HANAIMP_15 certification exam. However,

the concepts taught here are applicable to any of the older versions. As the basic concepts

stay the same and with coverage of SPS 04 included, we expect this book to be helpful for

future exams, although it won’t be tailored specifically to them.

You’ll notice in Table 1.2 that the C_HANAIMP_1 certification exam included _1 at

the end of the name, showing that it was the first certification exam. Over time,

SAP HANA Application Certification Exam SAP HANA SPS Year

C_HANAIMP_1 First few releases 2012

C_HANAIMP131 SAP HANA 1.0 SPS 05 2013




D_HANAIMP_10 SAP HANA 1.0 SPS 10 2015

C_HANAIMP_11 SAP HANA 1.0 SPS 11 2016





Table 1.2 SAP Certified Application Associate – SAP HANA Exam Names


SAP started including the year in the certification exam name—for example,

C_HANAIMP151. The first two numbers of 151 refer to the year, 2015, and the last

number, 1, refers to the first half of the year. In this case, this combination corre-

sponds to SPS 09.

In 2015, the naming convention was updated to refer directly to the SAP HANA

SPS number. That is, the SPS 12 exam was called C_HANAIMP_12 instead of

C_HANAIMP162. The _12 suffix indicates SPS 12.

With the new SAP HANA 2.0 releases, the numbering continues on from 12. As this

exam is the third one for SAP HANA 2.0, the exam was called C_HANAIMP_15.

SAP Education delivered delta exams for SAP HANA, which will make it easier to

extend the life of your SAP HANA certification. The D prefix in Table 1.2 indicates a

delta certification exam. You needed to have passed the C_HANAIMP151 certifica-

tion exam before you could take the D_HANAIMP_10 delta exam.

Tip

The SPS version for the on-premise version of SAP HANA, which we’re referring to in this

book, differs from that of the cloud version. We’re using SAP HANA 2.0 SPS 03. The corre-

sponding version on SAP Cloud Platform is SAP HANA 2.0 SPS 04.

Now that we’ve introduced the SAP Certified Application Associate – SAP HANA

certification, the next section will look at the exam as a whole: its objective, struc-

ture, and general process.

Exam Objective

In Figure 1.1, the official name of the certification examination is shown in all

uppercase letters as C_HANAIMP_15. On the left, you can see that the Level is Asso-

ciate.

On the right side, you can see that the exam has 80 questions. The Cut Score in this

example page is 64%, which means that you need to get 64% in this certification

examination to pass. The Duration of the exam is 180 mins. The exam is available

in English and Korean.


Figure 1.1 The C_HANAIMP_15 Certification Exam Information Page


Note that there used to be a PDF link for sample questions. This has now been

replaced by a sample exam that uses the actual exam system you’ll use when writ-

ing the exam. Just click on View more next to Sample Questions to open the sample

exam. In this case, there are 10 sample questions you can look at to get an idea of

what the actual examination questions look like. We normally recommend that

you keep the sample questions in reserve for later; don’t dive in right away. You

can use the sample questions to judge whether or not you’re ready for the certifi-

cation examination.

Tip

As a general rule, if you say “Huh?” when you look at the sample questions, then you’re

not yet ready for the exam.

The examination itself is focused on certain tasks.

Exam Structure

When you scroll down through the web page shown in Figure 1.1, you’ll see how the

exam is structured and scored. Each of the topic areas are mentioned there, as well

as the percentage that each area contributes toward your final score in the certifi-

cation exam.

The first area is Text Processing and Predictive Modeling. When you expand this

area, you can see that the HA300 training course is available for studying the con-

tent required to answer questions on this topic. More than 12% (actually about 15%)

of the questions fall into the Text Processing and Predictive Modeling topic, which

means that there are roughly 12 questions in the certification exam on this topic.

The second area is Building calculation views. When you expand this area, you can

see that the HA300 training course is available. More than 12% of the questions fall

into the Building calculation views, which means that there are roughly 12 ques-

tions in the certification exam on this topic.

The fourth area, Technology, architecture and deployment scenarios of SAP HANA,

is roughly 10% (between 8% and 12%) of the exam, meaning that it covers about

eight questions. The HA100 training course is available for more information on

this topic. Other portions of the exam follow this same percentage determination

and provide short descriptions.


As you can see, SAP recommends three training courses: HA100, HA300, and

HA301.

Table 1.3 lists the different topic areas you need to study for the C_HANAIMP_15

certification exam, the weighted percentage for each section, the estimated num-

ber of questions, and the chapters that correspond to these topics from the book.

The exam structure percentages in this book will differ slightly from those shown

in Table 1.3 due to the sequence in which the materials are explained. For example,

you’ll find the answers to some optimization questions in the information views

chapter where the topic is first discussed. This helps to avoid needless repetition.

To avoid you having to page through the book, we might occasionally remind you

of some important concepts.

Topic Percent of Exam Estimated Number

of Questions

Corresponding

Chapters

Technology, architecture,

and deployment scenarios of

SAP HANA

8%–12% 8 3, 9

Management and administration

of models

8%–12% 8 4

Building calculation views > 12% 12 5, 6

Detailed modeling functions > 12% 12 7

SQL and SQLScript in models < 8% 4 8

Text processing and predictive

modeling (advanced data

processing)

> 12% 12 10

Spatial, graph and series data

modeling (advanced data

processing)

8%–12% 8 11

Optimization of models 8%–12% 8 12

Security in SAP HANA modeling < 8% 4 13

SAP-supplied virtual data models < 8% 4 14

Table 1.3 C_HANAIMP_13 Exam Scoring Structure


Exam Process

You can book the certification exam you want to take at http://s-prs.co/v487606.

You can see a list of available certification exams at http://s-prs.co/v487607.

On the SAP Training website (https://training.sap.com/), choose the country you

live in or the country that you want to take the exam in. You can also choose to

write the exam in the cloud, using the Certification Hub subscription. The price of

the certification exam and available dates will appear in the right column on the

web page. Currently, the SAP HANA certification exams are also still administered

in SAP examination centers even though the exam is an online exam.

Note

New certification exams, such as C_HANAIMP_15, are administered in the cloud. Cloud

certifications use remote proctoring, in which an exam proctor watches you (the test

taker) remotely via your computer’s webcam. You have to show the proctor the entire

room before the exam begins by moving your webcam or your computer around. These

test proctors have been trained to identify possible cheating.

When you arrive for the exam, you’ll need a form of identification. During your

certification exam, you won’t have access to online materials, books, your mobile

phone, or the Internet.

As you mark answer options, the answers are immediately stored on a server to

ensure that you don’t lose any work. When you finish all your certification exam

questions and submit the entire exam, you receive the results immediately. If you

don’t pass the certification exam, you can take it up to two more times. (This

retake option hopefully won’t be important to any of the readers of this book!)

Normally, you’ll receive the printed certificate about a week later at your examina-

tion center, or you can receive it by mail. You can also see your certificate in the

Acclaim portal. You can find more information on how to register yourself at their

website at https://www.youracclaim.com/. The Acclaim platform is used by several

large companies to manage people’s certifications. Your certification status will be

shown here, and it allows potential customers or employers to verify your certifi-

cations. The Acclaim portal also allows you to share your SAP Global Certification

digital badges on major social networks like Facebook and LinkedIn. You can share

and promote all your certifications from SAP and other companies to give the eco-

system a comprehensive overview of all your skills.

http://s-prs.co/v487606


https://training.sap.com/

https://www.youracclaim.com/

Summary Chapter 1 37

Summary

You should now understand the various SAP HANA certification examinations

and be able to identify which exam is right for you. You know about the scoring

structure of C_HANAIMP_15, so you can focus your study time and energy accord-

ingly.

As you work through this book, we’ll guide you through the questions and content

that can be expected.

Best wishes on your exam!

Chapter 2

SAP HANA Training


� Identify SAP Education training courses for SAP HANA

� Find related SAP courses

� Discover other sources of information and courses on the

Internet

� Set up your own SAP HANA system to get practical experience

� Develop strategies for taking your SAP certification exam

Chapter 2 SAP HANA Training40

In this chapter, we’ll provide an overview of options for available resources and

training courses to prepare for your certification exam. We’ll look into SAP Educa-

tion, which makes SAP HANA training courses available for each certification and

provides related courses that can enhance your skills and understanding. We’ll

also discuss various sources on the Internet that provide SAP HANA documenta-

tion, video tutorials, ways to get hands-on experience, and free online courses.

Finally, we’ll review some techniques for taking the certification exam.

SAP Education Training Courses

You can attend SAP official training courses in a couple of different ways. These

training course options provide flexibility for learning and access to relevant

materials, so you can customize your studies to your lifestyle.

These different types of training courses include the following:

� Classroom training

The first and most obvious option is classroom training, in which you attend

SAP HANA courses in a classroom with a trainer for a few days. Classroom train-

ing courses provide a printed manual and a system through which you can

practice and perform exercises. At the end of the course, you’ll walk away with a

better understanding of what is described in that training material.

Classroom training is a popular option that allows individuals to focus on learn-

ing in an environment in which they can ask questions, perform exercises, dis-

cuss information with other students, and get away from their offices and

emails.

� Virtual classrooms

You can also attend SAP courses via virtual classrooms. The virtual approach is

similar to training in real-life classrooms, but you don’t sit in a physical class-

room with a trainer. Instead, your trainer teaches you via the Internet in a vir-

tual classroom. You still have the ability to ask questions, chat online with other

students, and perform exercises.

� E-learning

You can participate in the same training courses via e-learning as well. In this

case, you’re provided with a training manual and an audio recording of course

presentations. However, you don’t have an instructor to ask questions of, and

there is no interaction with others who are learning the same topic. Given that

this type of training normally happens after hours, it requires some discipline.

SAP Education Training Courses Chapter 2 41

� SAP Learning Hub

The last training course option is to use the SAP Learning Hub, a service you

subscribe to yearly. Your subscription grants you access to the entire SAP port-

folio of e-learning courses across every topic in the cloud, training materials,

some vouchers for taking certification exams, learning rooms, hands-on train-

ing systems, and forums for asking questions. You can find further details about

the SAP Learning Hub at their website at https://training.sap.com/learninghub.

Tip

After you’ve joined the SAP Learning Hub, we recommend that you join the SAP HANA

Modeling Learning room. This is by far the largest and most active room, and it’s very

helpful while preparing for this exam. The SAP people managing this room have regular

webinars on exam topics, provide sample questions, and quickly answer everyone’s ques-

tions.

You can also look at the Learning Journey for SAP HANA modeling at http://s-

prs.co/v487600. This shows the preceding points to you in a semi-graphical chart.

In the next two sections, we’ll discuss SAP HANA training courses specific to the

certification exams and additional courses related to SAP HANA that may prove

useful in your learning.

Training Courses for SAP HANA Certifications

Table 2.1 lists the SAP HANA training courses for the latest C_HANAIMP certifica-

tion exams, the length of each course, and each course’s prerequisites, if any.

Certification SAP Training Course Length Prerequisites

C_HANAIMP_14 HA100, collection 14 2 days N/A

HA300, collection 14 5 days HA100


C_HANAIMP_15 HA100, collection 15 2 days N/A



Table 2.1 SAP Training Courses for C_HANAIMP Certification Exams

https://training.sap.com/learninghub




The HA100 training course is the two-day introductory course that everyone must

take, regardless of which direction you want to go with SAP HANA. HA100 pro-

vides a quick introduction to SAP HANA architecture, the different concepts of in-

memory computing, and basic SAP HANA modeling concepts, data provisioning

(how to get data into SAP HANA), and how to use SAP HANA information models

in reports.

The HA300 training course goes into more detail on the modeling concepts and

the security aspects of SAP HANA.

As of SAP HANA SPS 02 (collection 14), the HA301 training course was added. The

SAP HANA advanced modeling topics, previously found in the HA300 course,

were moved to this new three-day training course, expanded with more content,

and these topics now contribute 25% to the C_HAMAIMP_15 certification exam.

You’ll need to take the HA100, HA300, and HA301 courses to prepare for the certi-

fication exam.

Tip

All the answers to the associate-level SAP HANA certification exams are guaranteed to be

somewhere in the official SAP training material.

Additional SAP HANA Training Courses

The following SAP training courses related to SAP HANA modeling can comple-

ment your skills and knowledge of SAP HANA:

� HOHAM1

This two-day course teaches modelers that used SAP HANA 1.0 how to migrate

their old (legacy) information models from SAP HANA extended application

services, classic model (SAP HANA XS) to SAP HANA extended application ser-

vices, advanced model (SAP HANA XSA). It’s a Virtual Live Classroom (VLC)

training course, where the trainer teaches the course via an online platform.

The course is practical, hands-on migration training—hence, the HO (for

“hands-on”) in the name.

� HA450

This three-day course merges SAP HANA modeling with the native application

development that you can perform with SAP HANA’s application server. It

teaches how you can take an SAP HANA information model, expose it as an

OData or REST web service, and consume it in the JavaScript framework called

Other Sources of Information Chapter 2 43

SAPUI5. You can also write the C_HANADEV certification exam after attending

this training course.

� UX402

This course complements the HA450 training course. It focuses on developing

user interfaces (UIs) with SAPUI5.

� HA215

If you want to learn about SAP HANA performance tuning, the two-day HA215

course complements the HA100 and HA300 courses.

� BW362

If you come from an SAP Business Warehouse (SAP BW) background, BW362 is a

related course that might help you. This course shows how you can build infor-

mation models with SAP BW on SAP HANA.

� HA400

If you come from an ABAP background, you should think about attending the

HA400 training course. It takes the knowledge that you gained in HA100,

HA300, and HA301 and shows you how to apply that knowledge in your ABAP

development environment. You’ll learn that the ways in which you access the

SAP HANA information models and interface with the SAP HANA database are

completely different from the ways to perform similar tasks in all the other

databases you’ve used through the years.

SAP Education offers a wide variety of courses to enhance your skills and further

your career. However, it’s important to know what resources are available outside

the classroom as well. In the next section, we’ll look at additional resources for

continued learning.

Other Sources of Information

You’ll find that there is no shortage of information about SAP HANA. In fact, so

much information is available that it’s almost impossible to get through it all. To

help focus your search, let’s look at some of the most popular sources.

SAP Help

SAP Help (http://help.sap.com) is a valuable resource for your SAP and SAP HANA

education. A lot of great documentation is provided on this website. At http://

http://help.sap.com



s-prs.co/v487608, you’ll find all available documentation provided in PDF format

(see Figure 2.1) in the Documentation Download area.

Figure 2.1 SAP HANA Documentation on SAP Help

Useful PDF files include the following:

� SAP HANA Modeling Guide for SAP HANA XS Advanced Model

We highly recommend this modeling guide, which provides the foundation for

working with and building SAP HANA information models. There are two other

modeling guides as well, namely for SAP HANA Studio and the Web Workbench.

We recommend that you focus on the SAP HANA XSA version.



� SAP HANA Interactive Education (SHINE) for SAP HANA XS Advanced

This guide is found when you click on the View All button in the Development

area of the screen. SHINE is a demo package that you can install into your own

SAP HANA system or your company’s SAP HANA sandbox system. It provides a

lot of data and models, with examples of how to create good information mod-

els. The example screens in this book make use of the SHINE package. We’ll look

at SHINE later in this chapter.

� SAP HANA Security Guide

This guide tells you everything you need to know about security in more detail.

We’ll discuss what you need to know about security for the exam in Chapter 13.

� SAP HANA Troubleshooting and Performance Analysis Guide

Learn how to properly troubleshoot your SAP HANA database and enhance

overall performance with this guide.

� What’s New in the SAP HANA Platform 2.0

This document lists the new features in the Support Package Stacks (SPSs) of

SAP HANA 2.0 since SPS 00.

� SAP HANA reference guides

There is an entire section of reference guides that provide more specific and

focused discussions of particular SAP HANA topics:

– SAP HANA SQLScript Reference

– SAP HANA XS JavaScript Reference

– SAP HANA XS JavaScript API Reference

– SAP HANA XS DB Utilities JavaScript API Reference

– SAP HANA Business Function Library (BFL)

– SAP HANA Predictive Analysis Library (PAL) Reference

� SAP HANA Developer Quick Start Guide

This specific developer’s guide is presented as a set of tutorials.

� SAP HANA Developer Guide for SAP HANA XS Advanced Model

We recommend reviewing this guide, especially because development and

information modeling are closely linked in SAP HANA. This guide teaches you

how to build applications in SAP HANA, write procedures, and more. This

includes information on building UIs using SAPUI5.

For some of these documents, it might be helpful to have an actual system to play

with because some guides provide step-by-step instructions for certain actions.

We discuss how to get your own SAP HANA system in a later section.


SAP HANA Home Page and SAP Community

The main website for SAP HANA is found at www.sap.com/products/hana.html,

which offers the latest news about SAP HANA and its different use cases. Along

with the SAP HANA home page, the SAP Community provides a central location

for members of different SAP communities and solution users. At http://s-prs.co/

v487609, you can ask questions about your own SAP HANA system, and SAP

employees will answer them. In addition, take a look at http://s-prs.co/v487610

where many other community support options are described.

SAP HANA Academy

From http://s-prs.co/v487611 or using a Google search, you can find a link to the

SAP HANA Academy. The SAP HANA Academy area of YouTube provides hundreds

of free video tutorials on all topic areas of SAP HANA, which you can access directly

via www.youtube.com/user/saphanaacademy/playlists. Figure 2.2 shows the play-

lists screen for the SAP HANA Academy.

These videos are created by SAP employees who perform actual tasks on an SAP

HANA system. If you’ve never made a backup of an SAP HANA system, for exam-

ple, you can search for a video on how to do just that, with step-by-step instruc-

tions performed on an actual system.

Because there are so many video clips available, we recommend that you select the

Playlists option from the YouTube menu bar, as shown in Figure 2.2. Alternatively,

you can go to the URL provided.

With each new SAP HANA release, new videos are added to the SAP HANA Acad-

emy YouTube channel, as shown in the SAP HANA SPS - What’s New row in Figure

2.2.

You’ll also see other playlists here that are worth investigating, for example, Appli-

cation Development and Delivery (Application Developer) and Predictive Analysis

(Data Scientist), as shown in Figure 2.3. Many of the advanced modeling topics dis-

cussed in the HA301 training course, such as graph and spatial modeling, are

examined in individual playlists.

http://www.sap.com/products/hana.html





http://www.youtube.com/user/saphanaacademy/playlists


Figure 2.2 SAP HANA Academy at YouTube

Figure 2.3 Playlists for Application Development and Predictive Analysis


You can also find videos on the SAP HANA Academy for building solutions. On the

Playlists page, look for a playlist called Building Solutions: Live5 under the Building

Solutions playlist collection (see Figure 2.4). This will guide you to build a complete

social media analysis solution for analyzing Twitter feeds in real time.

Figure 2.4 Playlists for Building Solutions in SAP HANA Academy

As noted earlier, the http://help.sap.com website provides all the PDFs for you to

read, which is great if you like reading and you want the PDFs on your tablet or

phone, for example. On the other hand, the SAP HANA Academy YouTube channel

is good if you prefer visual learning, and it may be more practical.

openSAP

Next up to consider is the openSAP website (https://open.sap.com/), which pro-

vides many free training courses on a large variety of SAP topics (see Figure 2.5).

This website frequently adds new online training courses, some of which focus on

SAP HANA (http://s-prs.co/v487612). When new training courses become available,

you can enroll in them. Every week, you’ll receive a few video clips. These courses

are normally four to six weeks long, and every week you’ll take a test. All the tests

together are worth 50% of your total score. In the last week, you take an exam that

is worth the other 50% of your score. At the end of the course, you receive a certif-

icate of attendance, and if you performed well, the certificate will show your score.

If appropriate, the certificate will also show that you were in the top 10% or top

20% of your class.

Note

The openSAP certificates don’t hold the same weight as the official SAP Education certifi-

cations.

http://help.sap.com

https://open.sap.com/


Hands-On with SAP HANA Chapter 2 49

Figure 2.5 openSAP Website

After you complete one of these free online courses, you can retake the tests or

exams for a fee, but you can still access all of the other course materials for free.

You can always download the video clips, the PowerPoint slides, and the tran-

scripts for your records.

Hands-On with SAP HANA

Everyone learns differently. Some people like visuals, others learn by listening,

and still others learn by doing. In this section, we look at how to gain some hands-

on experience with your own SAP HANA system, projects, and data. We’ll begin

with the different ways you can personally access an SAP HANA system.


Where to Get an SAP HANA System

On the SAP Developer Center site (https://developers.sap.com/), you can download

a free SAP HANA system or access your own SAP HANA server in the cloud via the

following link: http://s-prs.co/v487613, as shown in Figure 2.6. To do so, select

Install your free SAP HANA instance on this page.

Figure 2.6 SAP Developer Center Website for SAP HANA

Selecting that option takes you to the page at http://s-prs.co/v487614, where you’ll

find links to download the free SAP HANA, express edition; sign up for the free SAP

Cloud Platform; or use Microsoft Azure, Amazon Web Services (AWS), or Google

Cloud Platform (GCP) for your own hosted SAP HANA, express edition (see Figure

2.7).

The interactive graphic in Figure 2.7 will help you decide which of the many

options to choose. When preparing for the certification exam, you’ll need the

Server + XSA Applications option on the right side. You can install your own system

using the On-Premise options or get a cloud-hosted solution via the Cloud Provid-

ers options.

https://developers.sap.com/




Figure 2.7 SAP HANA System Options

In the next sections, we’ll look at the following options in greater detail: using a

free SAP Cloud Platform account, paying a cloud provider for the computing

resources, and two options for running SAP HANA on your own machine.

Free SAP Cloud Platform Account

Let’s start with the cheapest and easiest way to get your own SAP HANA system:

the free SAP Cloud Platform account. Figure 2.8 shows https://cloudplat-

form.sap.com, where you can get a free account on SAP Cloud Platform. After a

quick registration process, you’ll gain access to SAP Cloud Platform.

To learn how to set up your own account on SAP Cloud Platform, you can read the

Get Started with SAP Cloud Platform information at http://s-prs.co/v487615.

The free account on SAP Cloud Platform has restrictions. You don’t get a lot of

memory for loading large data sets, and the cloud platform is shared with many

other users. SAP Cloud Platform is an excellent choice to get going quickly, and it

doesn’t cost you anything. However, for the purposes of preparing for the certifi-

cation exam, one of the other options will probably be better.

https://cloudplatform.sap.com

https://cloudplatform.sap.com



Figure 2.8 SAP Cloud Platform Registration

SAP HANA on Third-Party Platforms

The next option is to get your own SAP HANA system on AWS, Microsoft Azure, or

GCP. While SAP HANA, express edition is free from SAP, you’ll need to pay these

cloud providers for hosting your solution.

All these cloud offerings are ideal for learning more about SAP HANA in a practical

manner. They’re quite inexpensive, as long as you remember to switch things off

when you’re finished—that is, essentially pressing a pause button on your SAP

HANA system. Otherwise, the cloud providers will continue charging you for the

CPU, memory, network, and disk space being used. You can also put a limit on your

finances to ensure you don’t go over your budget.

You can create an account on any of these cloud offerings via your own account

with the respective companies. An easy way to create your own SAP HANA system

on any of them is by using the SAP Cloud Appliance Library at https://cal.sap.com.

Registration is free on this website. After you have logged on, when scrolling down

the page, you’ll see a description for SAP HANA, express edition, with a Create

Instance button.

You can select with which Cloud Provider you want to create your SAP HANA sys-

tem, as shown in Figure 2.9. You can select the size of the SAP HANA server you

https://cal.sap.com


require as well. You can play around with the various options, and based on the

options you select, the cost per hour and the total monthly costs will be displayed.

Figure 2.9 SAP Cloud Appliance Library

Note

The SAP HANA editions on the SAP Cloud Appliance Library aren’t always the latest ver-

sions.

The SAP Cloud Appliance Library will also allow you to automatically switch your

SAP HANA system on and off at predefined times. The savings are also automati-

cally reflected in the monthly cost calculations.

When you’re satisfied with your choices, you can ask the SAP Cloud Appliance

Library to create your SAP HANA system automatically for you with your chosen

cloud provider.

If you have a Microsoft Visual Studio subscription, you get a certain amount of

credit each month for Microsoft Azure Cloud, which you can use to pay for your

SAP HANA, express edition system. Figure 2.10 shows you how to spin up an SAP

HANA, express edition system. Simply click on Create a resource in the top left of

the screen; type “SAP HANA” in the search box; select the SAP HANA, express edi-

tion (Server + Applications) option from the dropdown; and click the Create button

on the bottom right of the screen shown in Figure 2.10.


Figure 2.10 Creating Your Own SAP HANA, Express Edition System on Microsoft Azure

Running SAP HANA, Express Edition on Your Laptop or PC

The most-asked question from students through the years was for a copy of SAP

HANA that they can install on their laptops. You can now do exactly that!

SAP HANA, express edition is compiled so that it can run on your laptop with all

the functionality of the full version of SAP HANA while using less memory. Best of

all, it’s free for both development and productive use up to 32 GB of RAM.

SAP HANA, express edition is available as both a binary installer and a virtual

machine image to get you started quickly, but the easiest way to get started is to

use the virtual machine image. In the following steps, we’ll discuss how to get you

up and running quickly.

Tip

The screens and examples in this book were created with SAP HANA, express edition.

1. From http://s-prs.co/v487616, click on the Free Installation button, shown earlier

in Figure 2.6, or the buttons in the On-Premise block, shown earlier in Figure 2.7.



The alternative is to go directly to http://s-prs.co/v487617, register, and get the

download manager for SAP HANA, express edition. This program requires Java.

2. When you start the download manager for SAP HANA, express edition, as

shown in Figure 2.11, you can choose between a Virtual Machine image and a

Binary Installer. The default is the Virtual Machine image.

Figure 2.11 Download Manager for SAP HANA, Express Edition 2.0

3. Select the functionality you want to use with your copy of SAP HANA, express

edition.

Note

You need a minimum of 8 GB memory to run SAP HANA, express edition. You might need

16 GB if you want to use SAP HANA XSA. We recommend that you install the Server +applications virtual machine, as shown in Figure 2.11. This option includes SAP HANA

XSA and is what we use in this book.



Options you might select include the following:

– The first selection on the list is the Getting Started with SAP HANA express

edition (Virtual Machine Method) PDF file. This document will help you install

your copy of SAP HANA, express edition. You can also follow the instructions

at http://s-prs.co/v487618.

– The Virtual Machine option already includes the SUSE Linux Enterprise

Server (SLES) operating system. If you use the Binary Installer option, you’ll

need to download a certified operating system. See the next section for links.

4. Ensure you have the VMware Workstation Player (for Windows and Linux) or

VMware Fusion (for Mac).

Tip

The VMware Workstation 15 Player is free for personal use and can be downloaded at

their website at www.vmware.com/go/downloadplayer/.

After you’ve installed SAP HANA, express edition, you need to set up the environ-

ment so that you can start using it for information modeling. You can get guid-

ance by following the tutorials for SAP HANA, express edition at http://s-prs.co/

v487619 or by following the steps described in the PDF you downloaded previously

per Figure 2.11. However, the following steps will help you quickly set up your SAP

system so that you can get to the screens we’ll show in this book:

1. Make sure your hosts file contains a reference to hxehost as described in the

Getting Started guide.

2. Check if the XS engine is up and running by opening http://hxehost:8090 in

your browser.

Tip

We recommend that you use the Google Chrome browser.

3. Check that SAP HANA XSA and services are running by opening https://hxe-

host:39030. This will show you a list of apps that are running and their web

addresses. You can open any of these apps by clicking on their web addresses.

Check that the xsa-cockpit and webide apps are running as shown in Figure 2.12.

If you see the web app running, it means you’ve also installed the SHINE demo

application. Many of the demo screens we show in this book use the SHINE

demo application.


http://www.vmware.com/go/downloadplayer/



http://hxehost:8090

https://hxehost:39030

https://hxehost:39030


Figure 2.12 SAP HANA XSA Up and Running with Available Apps

4. Create your own user by opening the SAP HANA XS Advanced Cockpit app screen

and logging in with the XSA_ADMIN user. You’ll see a web application, as shown

in Figure 2.13. Click User Management, and create a new user by clicking on the

New User button.

Figure 2.13 SAP HANA XS Advanced Cockpit App


5. Add your new user to the development space.

6. Open the Organizations screen, and select HANAExpress. You’ll see two spaces

named development and SAP, as shown in Figure 2.14.

Figure 2.14 SAP HANA XSA Administration App: Organization and Space Management

7. Select the development space. Select the Members option on the menu. You

should see that the XSA_ADMIN and XSA_DEV users are already in this space.

8. Click on the Add Members button, and enter your user name.

9. Select the Space Developer checkbox next to your user name, and click the OK

button. Your user is now added to the development space.

10. Open the webide app. We’ll spend a lot of time in SAP Web IDE for SAP HANA.

In Chapter 4, we’ll describe it in more detail.

11. Log in with your new user name. SAP Web IDE for SAP HANA won’t have any

content for your new user. To get up and running quickly, you can clone or

import the source code of the SHINE SAP HANA XSA demo application. A few

tips are as follows:

– The easiest way to get the source code for SHINE is to clone it from GitHub.

Open the menu, and select File • Git • Clone Repository. When asked for a Git

Repository, enter the public GitHub address where SAP HANA SHINE resides,

that is, “https://github.com/SAP/hana-shine-xsa”. SAP Web IDE for SAP

HANA will download all the source code, information models, and design-

time objects for you.

https://github.com/SAP/hana-shine-xsa


– If you’ve already installed SHINE during the SAP HANA install, open the

Web app, and click on the icon in the top-right corner. This will download a

ZIP file to your local machine with the SHINE source code. In the webide app,

open the menu, and select File • Import • File or Project. Then select the ZIP

file from SHINE on your local machine.

12. Next, set the space where you want to work in the Project Settings for your

project. As shown in Figure 2.15, select the second-level node (which is your

project), and right-click on the name. In the popup context menu, select Project •

Project Settings.

Figure 2.15 SAP Web IDE for SAP HANA: Configuring Project Settings for a Project

13. On the next screen, select Space, and then choose the development space from

the dropdown list. This is the same space you added your user to earlier. Click

Save and then Close.

14. The last step is to build your SHINE application. At the moment, you only have

the source code, but you want it to create a schema, users, tables with data,

information models, and much more for you. This is accomplished by building

your project. As shown in Figure 2.16, select the second-level node (your proj-

ect), and right-click on the name. In the popup context menu, select Build •

Build.

15. After the core-db module has been built, you can open it. Open the src folder,

then the models folder, and open the PRODUCTS.hdbcalculationview model.

You should see something that looks like Figure 2.17.


Figure 2.16 SAP Web IDE on SAP HANA: Building the SHINE Demo Application

Figure 2.17 SAP Web IDE on SAP HANA: Showing an SAP HANA Graphical Information Model

Congratulations! You now have a fully working SAP HANA system.


Building an SAP HANA System

You can also install SAP HANA on a server. You’ll need some certified hardware, an

operating system, and the SAP HANA software. Note that you’ll need a registered

S-user name and password before you can download the SAP HANA software. If

you’re an existing SAP customer or SAP partner, you can ask your system admin-

istrator for a logon user.

The requirements for the hardware and how to install the SAP HANA software can

be found at http://s-prs.co/v487620.

Note

It’s recommended that you use a machine with a minimum of 24 GB or 32 GB of memory.

If you have less memory, you should use SAP HANA, express edition.

The certified operating systems for SAP HANA are SLES for SAP Applications and

Red Hat Enterprise Linux for SAP HANA. You can find the SLES for SAP Applica-

tions at www.suse.com/products/sles-for-sap/, or you can get a copy of Red Hat

Enterprise Linux for SAP HANA from https://developers.redhat.com/products/sap/

overview/.

You’ll also need to download a copy of the SAP HANA software from http://s-

prs.co/v487621. In the alphabetical list of products, select H, choose SAP HANA Plat-

form Edition, and then follow the instructions in installation guides on http://s-

prs.co/v487620.

Project Examples

After you have your own SAP HANA system, the next step is to start using it. In this

section, we’ll discuss how to start working on SAP HANA projects and how you can

use the SHINE demo package or create your own project.

Using the SHINE Demo Package

One option to get some practice in SAP HANA is to explore the content available in

the SHINE demo package. We’ll use the SHINE demo throughout this book. It’s

fully documented and is used by SAP as an example for how to develop SAP HANA

applications. You can see what the SHINE demo application looks like in Figure

2.18.


http://www.suse.com/products/sles-for-sap/

https://developers.redhat.com/products/sap/overview/

https://developers.redhat.com/products/sap/overview/






Figure 2.18 SHINE (SAP HANA Interactive Education) for SAP HANA XS Advanced Model Screen

The SHINE documentation can be found at http://s-prs.co/v487620 in the Develop-

ment area by clicking the View All button.

Creating Your Own Project

You can also think up your own projects. You can start very simply by just learning

to work with individual topics discussed in the book, such as fuzzy text search, cur-

rency conversion, input parameters, hierarchies, and spatial joins. You can also

add some security on top of this, for example, to limit the data for a single user to

just one year.

The next step is to think about creating a report that can analyze the performance

of a particular data set. This is a good exercise because you’ll have to take end-user

requirements and learn how to translate them into the required SAP HANA infor-

mation models. Then, you’ll design and create these models. Finally, you can

expand these information models into an application.

You can also look at incorporating something such as SAP Lumira to easily create

attractive storyboards. You can download a free copy of SAP Lumira from https://

saplumira.com/.


https://saplumira.com/

https://saplumira.com/


Where to Get Data

SAP HANA can process large amounts of data. To get hands-on practice in the SAP

HANA system, you’ll want some sort of data to play with. There are a couple of

places where you can acquire such data:

� SHINE demo package

The SHINE demo package is a great place to get large data sets. To do this in the

SHINE package, use the Data Generator option (the first option in Figure 2.18,

shown previously).

� Datahub

Datahub is a website and free open data management system that can be used

to get, use, and share data. At https://datahub.io/ (see Figure 2.19), you can find

more than 10,000 data sets. (If you can’t find what you’re searching for, look at

the old site at https://old.datahub.io/dataset.)

Figure 2.19 Datahub

� US Department of Transportation database

If you want a larger data set to play with, you can download the US Department

of Transportation database on flights in the United States at http://s-prs.co/

v487622. It has more than 50 million records, with more than 20 years of data,

https://datahub.io/

https://old.datahub.io/dataset




including departure airport, destination airport, airline, flight number, airborne

time, flight delays and cancellations, and so on. You can use this data to experi-

ment with the predictive capabilities of SAP HANA described in Chapter 10, for

example, to predict whether your US flight will be delayed and by how much.

Exam Questions

Now, we’ll change gears a bit and look at the SAP certification exams and how to

approach them. Let’s look at the examination question types and some tips and

hints about how to complete the certification exam.

Types of Questions

An international team sets up the SAP HANA certification exams. All the questions

are written in English, and all the communication is in English. Because the exam

teams are international, a lot of attention is focused on making sure that every-

body will understand what is meant by each question, avoiding possible double

meanings or ambiguities. Some of the exams get translated into other languages,

so simple and straightforward questions work best.

Figure 2.20 shows three types of questions that you’ll find in the certification

exam.

Figure 2.20 Three Types of Certification Exam Questions

Which of the following are components of SAP HANA Data Warehousing Foundation?Note: There are 3 correct answers to this question.

Which of the following approaches can be used to implement union pruning?Note: There are 2 correct answers to this question.

In your calculation view, you CANNOTenable a hierarchy for time dependency.What could be the reason for this?

The hierarchy is a parent-child type.

The hierarchy is a level type.

You did NOT reference a DIMENSION of the type TIME.

You did NOT include a historycolumn table.

Define a constant value for each datasource in the Union Node.

Define the cardinality between the datasources.

Define a restricted column and include it in both data sources of a union.

Define union pruning conditions in apruning configuration table.

Native DataStore Object

SAP Data Hub

CompositeProvider

Data LifeCycle Manager

Data Distribution Optimizer

Exam Questions Chapter 2 65

The first question is multiple choice. The radio buttons indicate that you can only

choose one of the four available answer options. There will be three or four differ-

ent options, with only one correct answer. (Most of the time there will be four

answer options. The newer exams have a few questions that only have three

answer options for this type of question. The older exams always had four

options.)

The other two questions are called multiple-response questions, of which there are

two types:

� With the first type of multiple-response question, there are four possible

answers, and there will always be two correct answers and two incorrect

answers. In the exam, this will be indicated by the words, “Note: There are two

correct answers to this question.”

� The other type of multiple-response question has five possible answers, of which

three will always be correct, and two will be incorrect; you must select three cor-

rect answers. Above the answers, you’ll see the words, “Note: There are three

correct answers to this question.”

Both types of multiple-response questions use checkboxes, and you must select

however many answers are correct.

All three types of questions have exactly the same weight. You either get a ques-

tion right or you get it wrong. It’s a binary system: 0 or 1.

For a multiple-response question with two correct and two incorrect answers, you

must select two answers. If you select three answers or one answer, you don’t

receive any points. If you select two answers and they are the correct two answers,

then you earn a point.

Tip

Select as many answers as are required! If you’re certain that an option is correct, select

it. If you’re doubtful about an answer, at least select the correct number of answers. If

you don’t select anything, you won’t earn any points. Even if you plan to come back to the

question later, we still recommend that you select the exact number of answer options

required.

Multiple-choice questions normally make up the majority of the questions for the

associate-level core exams. More than half of the questions will have only one cor-

rect answer.


Note

In the certification exam, there are no fill-in-the-blank questions. There are also no true

or false questions in the certification exam, but we’ll use true or false questions in this

book for training purposes.

The questions and the answers themselves appear randomly. In other words, two

people could be taking the same exam, sitting next to each other, and the order in

which the questions appear for each person would be different. In addition, for

similar questions, the order of A, B, C, and D (the answer options) is different for

different people. Because answer options are randomized, you’ll never find any

questions that list “All of the above” or “None of the above” as a possible answer.

The questions in the SAP certification exams tend to be very short and to the

point. All extra words and descriptions have been cut away, ensuring that you’ll

have enough time to complete the exams. We’ve never heard anyone complain

about the time limits in the SAP HANA certification exams.

Because the questions are to the point, every word counts. Every word is there for

a reason and has a purpose, so don’t ignore any word—especially words such as

always, only, and must. Always indicates on every occasion, only means that no

other options are allowed, and must indicates something that is mandatory. In the

answer options, you could see some optional actions that would be valid actions in

normal circumstances, but if they aren’t mandatory, they are incorrect answers for

questions asking what you must do. Therefore, for questions using the word must,

pay attention to optional steps versus mandatory steps.

The certification exams use a minimum of negative words, which are only used for

troubleshooting questions. All negative words in a question are written in capital

letters—for example, “You did something and it is NOT working. What is the rea-

son?” You can see an example of this in the first question in Figure 2.20 shown ear-

lier in this section.

Tip

Don’t just learn the facts as if everything will always work properly. Systems break in real

life, and you need to know why they didn’t work and how to fix or troubleshoot the prob-

lem. There will be questions in the exam to test this knowledge.

Now that we’ve looked at the general structure of SAP certification exam ques-

tions, let’s look at the strategy of elimination in the exam.

Exam Questions Chapter 2 67

Elimination Technique

Experience has shown that the elimination technique can be useful for answering

exam questions. In this technique, you start by finding the wrong answers instead

of finding the right answers.

When creating exams, it’s fairly easy to set up questions, but it’s hard to think up

wrong answers that still sound credible. Because it’s so difficult to write credible

wrong answers, it’s normally easier to eliminate the wrong answers first. As Sher-

lock Holmes would say, whatever remains, however improbable, must be the

truth.

To show how efficient this technique can be, look at the multiple-response ques-

tion with five different options (refer to Figure 2.20, right). There are three correct

and two incorrect answers, so it saves you time to find the two incorrect ones,

rather than trying to find the three correct answers.

Tip

Make sure you know which keywords apply to which area. The wrong answers could

come from an unrelated area, but the words sound like they might belong to the area in

which the question is asked. Don’t confuse the words from different topic areas.

Bookmark Questions

In the certification exam, you can always bookmark questions and return to them

later. Figure 2.21 shows the sample exam you can write for C_HANAIMP_15. You

can see the Assessment Navigator block in the bottom-right corner of the screen.

You’ll notice that the top-left corner of question 1 is marked. By clicking on the

bookmarked question in the Assessment Navigator, it will take you directly to that

question.

If you’re not confident about a question, mark it. We still recommend that you

complete the right number of answer options: If two answers are required, fill in

two options. If three are required, select three options. Then, mark the question so

that you can review it later.

Tip

Watch out when you do the review; your initial choice tends to be the correct one.

Remember, don’t overanalyze the question.


Figure 2.21 Sample Practice Exam: Assessment Navigator

Tip

Watch out that you don’t click the Submit button in the bottom right of the screen until

you’ve finished answering all the questions. This button doesn’t submit the answers to

each question, but ends the entire exam!

Now, let’s turn our attention to some general exam strategies. Implementing test-

taking strategies will help you succeed on the exam.

General Examination Strategies

The following are some good tips, tricks, and strategies to help you during an

exam:

� If you have the SAP training manuals, read them twice. Pay attention to pages

with lots of bullet points because that means there are normally lots of options,

which are likely good sources for questions. In addition, look carefully at the

Caution boxes.


� For this associate-level certification exam, when using this book, read the entire

chapters and answer all the questions. If you don’t get the answers right or don’t

understand the question, reread that section. This is the equivalent of reading

the SAP training materials twice.

� Make sure that you’ve answered all the questions and that you’ve selected the

correct number of answer options. If the question says that three answers are

correct, then make sure that you’ve marked three—not four or two.

� Occasionally, you’ll find answer options that are opposites—for example, X is

true, and X is NOT true. In that case, make sure you select one of the pair.

� In our experience with SAP HANA certification exams, the answers from one

question don’t provide answers to another question. SAP Education is careful to

make sure that this doesn’t happen in its exams.

� Watch out for certain trigger words. Alarm bells should go off when you see

only, must, and always.

� Look out for impossible combinations. For example, if a question is about mea-

sures, then the answer can’t be something to do with dimension calculation

views because you can’t find measures for such views.

� Some words can be different in different countries. For example, some people

talk about a right-click menu, but in other places in the world, this same feature

is known as a context menu. This is the menu that pops up in a specific context,

and you access this menu by right-clicking.

� Make sure you get a good night’s rest before the exam.

Summary

You should now know which SAP Education training courses you can attend for

your certification examination, which related SAP courses will complement your

knowledge and skills, and where to get hands-on experience. In addition, we intro-

duced many free sources of information on the Internet, training videos on You-

Tube, and online courses at openSAP.

You can now form a winning strategy for taking your SAP certification exam. In

the next chapter, we’ll begin looking at exam questions and concepts, focusing

first on the technology, architecture, and deployment scenarios of SAP HANA.

Chapter 3

Technology, Architecture, and Deployment Scenarios


� Understand in-memory technologies and how SAP HANA

uses them

� Get to know the internal architecture of SAP HANA

� Describe the SAP HANA persistence layer

� Learn the different ways that SAP HANA can be deployed

� Know when to use different deployment methods, including

the various cloud options

Chapter 3 Technology, Architecture, and Deployment Scenarios72

In this chapter, you’ll become familiar with the various deployment scenarios for

SAP HANA and evaluate appropriate system configurations, including cloud

deployments. We’ll introduce the in-memory paradigm and the thinking behind

how SAP HANA uses in-memory technologies, and you’ll get a glimpse of how to

develop information models with SAP HANA. By the end, you’ll better understand

how SAP HANA functions in all types of solutions and use cases you might require.

Real-World Scenario

You start a new project. The business users describe what they want and

expect you to suggest an appropriate architecture for how to deploy SAP

HANA. You have to decide between building something new in SAP HANA or

migrating the current system to use SAP HANA. You also have to decide if you

should use SAP HANA in the cloud and, if so, which type of service you’ll need.

When you understand the internal architecture of SAP HANA, it can lead to

unexpected benefits for your company. You can use the fact that SAP HANA

uses compression in memory combined with an understanding of how SAP

HANA uses disks and backups to provide a customer with an excellent busi-

ness case for moving its business solution to SAP HANA. This can lead to dra-

matically lower operational costs and reduced risk because the compression

lets SAP HANA use less memory, which leads to smaller backups and imme-

diately reduces the storage space required by the business (operational

costs). These backups take less time, don’t require any downtime, and can be

made at any time of the day, leading to reduced risk.

Objectives of This Portion of the Test

The objective of this portion of the SAP HANA certification test is to evaluate your

basic understanding of the various SAP HANA deployment scenarios and the cor-

responding system configurations. The certification exam expects SAP HANA

modelers to have a good understanding of the following topics:

� How SAP HANA uses in-memory technology, including elements such as col-

umns, tables, compression, delta buffer, and partitioning

� SAP HANA architecture concepts

� The persistence layer in SAP HANA

� The various ways that SAP HANA can be deployed

Key Concepts Refresher Chapter 3 73

� How SAP HANA can be deployed in the cloud

� The direction SAP is taking by allowing your modeling and development work

to run on both standalone and cloud deployments of SAP HANA

� An overview of using SAP HANA with analytics and business intelligence (BI)

Note

This portion contributes about 10% of the total certification exam score.

Key Concepts Refresher

We’ll start with something that most people know very well: Moore’s Law. Even if

you’ve never heard of it, you’ve seen its effects on the technology around you, for

example, with your mobile phone. Put simply, this means that the overall process-

ing power for computers doubles about every two years. Therefore, you can buy a

computer for roughly the same price as about two years ago, and it will be about

twice as fast and powerful as the older one. This held true for mobile phones as

well. (Lately Moore’s Law hasn’t kept up, and devices such as high-end mobile

phones have risen in price.)

Moore’s Law is useful for understanding system performance and response times.

If you have a report that is running slow, let’s say an hour long, you can fix this by

buying faster hardware. You can easily decrease the processing time of your slow

report from 1 hour to 30 minutes to 15 minutes by just waiting a while and then

buying faster computers. However, a few years ago that simple recipe stopped

working. Our easy upgrade path was no longer available, and we had to approach

the performance issues in new and innovative ways. Data volumes also keep grow-

ing, adding to the problem. SAP realized these problems early and worked on the

solution we now know as SAP HANA.

In this chapter, we’ll discuss SAP HANA’s in-memory technology and how it

addresses the aforementioned issues. We’ll then look at SAP HANA’s architecture

and approaches. Finally, we’ll walk through the various deployment options avail-

able, including cloud options and using SAP HANA for analytics.

In-Memory Technology

SAP HANA uses in-memory technology. But, just what is in-memory technology?

In-memory technology is an approach to querying data residing in RAM, as


opposed to physical disks. The results of doing so lead to shortened response

times, which enable enhanced analytics for BI/analytic applications.

In this section, we’ll walk through the process that leads us to use in-memory tech-

nology, which will illustrate the importance of this new technology.

Multicore CPU

After years of CPUs getting faster, we seemed to hit a wall when CPU speeds

reached about 3 GHz. It became difficult to make a single CPU faster while keeping

it cool enough. Therefore, chip manufacturers moved from a single CPU per chip

to multiple CPU cores per chip.

Instead of increasingly fast machines, machines now stay at roughly the same

speed but have more cores. If a specific report takes an hour and only runs on a sin-

gle core, that report won’t run any faster thanks to more CPU cores. We can run

multiple reports simultaneously, but each of them will run for an hour. To speed

up reports, we need to rewrite software to take full advantage of new multicore

CPU technology—that is, through parallel processing—which is a problem that

has been faced by most software companies.

SAP addressed this problem by making sure that all operations in SAP HANA are

making full use of all available CPU cores.

Slow Disk

When talking about poor system performance, you can place a lot of blame on

slow hard disks. Table 3.1 shows how slow a disk is when compared to memory.

Operation Latency

(in Nanoseconds)

Relative to Memory

Read from level 1 (L1) cache 0.5 ns 200 times faster

Read from level 2 (L2) cache 7 ns 14 times faster

Read from level 3 (L3) cache 15 ns 6.7 times faster

Read from memory 100 ns –

Send 1 KB over 1 Gbps network 10,000 ns 100 times slower

Read from solid state disk (SSD) 150,000 ns 1,500 time slower

Table 3.1 Latency of Hardware Components and Performance of Components Shown Relative to RAM


To give you an idea of how slow a disk really is, think about this: if reading data

from RAM takes one minute, it will take more than two months to read the same

data from disk! In reality, there is less of a difference when we start reading and

streaming data sequentially from disks, but this example prompts the idea that

there must be better ways of writing systems.

The classic problem is that alternatives such as RAM have been prohibitively

expensive, and there hasn’t been enough RAM available in a machine to replace

disks.

Therefore, the way we’ve designed and built software systems for the past 30 or

more years has been heavily influenced by the fact that disks are slow. This affects

the way everyone from technical architects to developers to auditors thinks about

system and application design. Consider these three examples of the wide effect of

slow disks:

� SAP NetWeaver

SAP NetWeaver architecture uses one database server and multiple application

servers (Figure 3.1).

Figure 3.1 SAP NetWeaver Architecture: One Database Server, Many Application Servers

Read from disk 10,000,000 ns 100,000 times slower

Send network packet from the US

West Coast to Europe and back

150,000,000 ns 1,500,000 times slower

Operation Latency

(in Nanoseconds)

Relative to Memory

Table 3.1 Latency of Hardware Components and Performance of Components Shown Relative to RAM (Cont.)

Database Server

ApplicationServer

ApplicationServer

ApplicationServer

Calculations Business Rules

DB Independent


Because the database stores data on disks that are slow, the database becomes

the bottleneck in the system. In any system, you always try to minimize the

impact of the bottlenecks and the load on the system. Therefore, we move the

data into multiple application servers to work with it there, using the database

server as little as possible. In SAP systems, we used it so little that we were data-

base independent. SAP systems even used their own database-independent

ABAP Data Dictionaries.

In the application servers, we run all the code, perform all the calculations, and

execute the business rules. We use memory buffers to store a working set of

data. In many SAP systems, 99.5% of all database reads come from these mem-

ory buffers, thus minimizing the effects of the slow disk. The SAP system archi-

tecture was heavily influenced by the fact that the disk is slow.

� Financial systems data storage

Inside financial systems, we store data such as year-to-date data, month-to-date

data, balances, and aggregates. For reporting, we have cubes, dimensions, and

more aggregates. These values are all updated and stored in financial systems to

improve performance.

Take the case of the year-to-date figure. We don’t really need to store this infor-

mation because we can calculate it from the data. The problem is that to calcu-

late the year-to-date figure, we need to read millions of records from the data-

base, which the database reads from the slow disk. Therefore, we could calculate

the current year-to-date figure, but doing so would be too slow. Because disk

storage is cheap and plentiful, we just add this value into the database in

another table. With every new transaction, we simply add the new value to the

current year-to-date value and update the database. Now reports can run a lot

faster on disk.

If the disk was fast, we would not need to store these values. Can you imagine a

financial system without these values? Ask accountants to imagine a financial

system without balances! Why store balances when you can calculate them?

When you see the reaction on their faces, you’ll understand how even their

thinking has been influenced by the effects of slow disks.

We’ll return to this example in the next section.

� Data warehouses

One of the many reasons we have data warehouses is that we need fast and flex-

ible reporting. We build large cubes with precalculated, stored aggregates that

we can use for reporting and to slice and dice the data. Building these cubes

takes time and processing power. Reading the masses of data from disk for the


reports can be slow. If we keep these cubes inside our financial system, for

example, then a single user running a large report from these cubes can pull the

system’s performance down. That one reporting user affects thousands of other

users.

Therefore, SAP created separate data warehouses to reduce the impact of

reporting on operational and transactional systems. Those reporting users can

now run their heavy, slow reports away from the operational and transactional

systems. The database world now is split into two parts: the online transac-

tional processing (OLTP) databases for the financial, transactional, and opera-

tions systems, and the online analytical processing (OLAP) data warehouses

(see Figure 3.2).

Figure 3.2 OLTP vs. OLAP: Keeping Operational Reporting Separate from Operational Systems for Performance Reasons

If disks were fast, and we could create aggregates fast enough, we would be able

to perform most of our operational reporting directly in the operational sys-

tem, where the users expect and need it. We would not have separate OLTP and

OLAP systems. Again, slow disks influenced architectural design principles.

These three examples prove that slow disks influenced system design, architec-

ture, and thinking for many years. If we could eliminate the slow disk bottleneck

from our systems, we could then change our minds about how to design and

implement systems. Hence, SAP HANA represents a paradigm shift.

SAP ERP,SAP CRM,

SAP SRM, …

DataWarehouse

Database(OLAP)

Database(OLTP)

Pre-calculated stored values


Loading Data into Memory

In the first of the three examples in the previous section, you saw that we’ve used

memory where possible to speed things up with memory caching. Nevertheless,

until a few years ago, memory was still very expensive. Then, memory prices

started coming down fast, and we asked, “Can we load all data into memory

instead of just storing it on disk?”

Customer databases for business systems can easily be 10 TB (10,000 GB) in size.

The largest available hardware when SAP HANA was developed had only 512 GB to

1 TB of memory, presenting a problem. SAP obviously needed to compress data to

fit it into the available memory. We’re not talking here about ZIP or RAR compres-

sion, which needs to be decompressed again before it’s usable, but dictionary com-

pression. Dictionary compression involves mapping distinct values to consecutive

numbers. Using this type of compression enables the data to be used in its com-

pressed format.

Column-Oriented Database

For years, it was common to store data in rows. Row-based databases work well

with spinning disks to read single records faster. However, when we start storing

data in memory, there are fewer valid reasons to use row-based storage. At that

point, we can decide to use row-based or column-based storage.

Why would we use columns instead of rows? When we start looking at compress-

ing data, then row-oriented data doesn’t work particularly well because we mix dif-

ferent types of data—such as cities with salaries and employee numbers—in every

record. The average compression we get is about two times (i.e., data is stored in

about 50% of the original space).

When we store the same data in columns, we now group similar data together. By

using dictionary compression, we can achieve excellent compression, and thus

column-based storage starts making more sense for in-memory databases.

Another way in which we can save space is via indices. If we look inside an SAP sys-

tem, we find that about half the data volume is taken up by indices, which are actu-

ally sorted columns. When we store the data in columns in memory, do we really

need indices? We can save that space. With such column-oriented database advan-

tages, we end up with 5 to 20 times compression.

Let’s get back to our original problem of 10 TB databases, but machines with only

1 TB of memory. By using column-based storage, compression, and no indices, we


can fit a 10 TB database into the 1 TB of available memory. Even though we focused

on the preferred column-oriented concept here, note that SAP HANA can handle

both row- and column-based tables.

Removing Bottlenecks

When we manage to load the database entirely into memory, exciting things start

happening. We break free from the slow disk bottleneck that had a stranglehold on

system performance for many years and can start exploring new architectures.

As always, even if our systems become thousands of times faster, at some stage,

we’ll reach new constraints. These constraints will determine what the new archi-

tecture will look like. The way we provide new business solutions will depend on

the characteristics of this new architecture.

Architecture and Approach

The new in-memory computing paradigm has changed the way we approach solu-

tions. Different architectures and deployment options are now possible with SAP

HANA.

Changing from a Database to a Platform

The biggest change in approach is how and where we work with the data. Disks

were always slow, causing the database to become a bottleneck. Thus, we moved

the processing of data onto application servers to minimize the pressure on this

disk bottleneck.

Now, data storage has moved from disk to memory, which is much faster. There-

fore, we needed to reexamine our assumptions about the database being the bot-

tleneck. In this new in-memory architecture, it makes no sense to move data from

the memory of the database server to the memory of the application servers via a

relatively slow network. In that case, the network will become the bottleneck, and

we’ll duplicate functionality.

New bottlenecks require new architectures. Our new bottleneck arises because we

have many CPU cores that need to be fed as quickly as possible from memory.

Therefore, we must put the CPUs close to the memory (i.e., the data). In doing so,

we realized that SAP HANA can be offered as an appliance. As shown in Figure 3.3,

it doesn’t make sense to copy data from the memory of the database server to the


memory of the application server as we’ve always done (left arrow). We instead

now move the code to execute within the SAP HANA server (right arrow).

Figure 3.3 Moving Data In-Memory

Our solution approach also needed to change. Instead of moving data away from

the database to the application servers—as we’ve done for many years—we now

want to keep it in the database. Many database administrators (DBAs) have done

this for years via stored procedures. Instead of stored procedures, in SAP HANA, we

use graphical information models, as we’ll discuss in Chapter 4 through Chapter 7.

You can also use stored procedures and table functions if necessary. We’ll look at

this in Chapter 8.

Note

Instead of moving data to the code, we now move the code to the data. We can easily

implement this process by way of SAP HANA graphical modeling techniques.

When you start running code inside the database, it’s only a matter of time before

it changes from a database into a platform. When people tell you that SAP HANA is

“just another database,” ask them which other database incorporates many pro-

gramming languages, a web server, an application server, an HTML5 framework, a

development environment, replication and cleaning of data, integration with

most other databases, predictive analytics, machine learning, and multitenancy—

the reactions can be interesting.

Application Server

Database Server

Calculations

Calculations

Move data to theapplication (code)

The OldApproach

Move code to thedatabase (data)

The NewApproach


Parallelism in SAP HANA

In the Slow Disk section, we provided three examples of the wide-ranging effects

of slow disks on performance. In the second example, we looked at calculating the

year-to-date value. We don’t really need to store a year-to-date value because we

can calculate it from the data. With slow disks, the problem is that to calculate this

value, we need to read millions of records from the database, which the database

reads from the slow disk. Because disk storage is cheap and plentiful, we just added

this aggregate into the database in another table.

When we have millions of records in memory and many CPU cores available, we

can calculate the year-to-date value faster than an old system can read it from the

new table from a slow disk. As shown in Figure 3.4, even though we require a single

year-to-date value, we use memory and all the available CPU cores in parallel to cal-

culate it.

Figure 3.4 Single Year-to-Date Value Calculated in Parallel Using All Available CPU Cores

Value 1

CPU core 1

Value 2…

…Value n

CPU core 2

CPU core 3

…

CPU core m

SUM of 1..n

SUM of 1..ncalculated in parallel


We just divide the memory containing the millions of records into several ranges

and ask each available CPU core to calculate the value for its memory range.

Finally, we add all the answers from each of the CPU cores to get the final value.

Even a single sum is run in parallel inside SAP HANA.

Although it’s relatively easy to understand this example, don’t overlook the

important consequences of this process: if we can calculate the value quickly, we

don’t need to store it in another table. By applying this new approach, we start to

get rid of many tables in the system, which then leads to simpler systems. We also

have to write less code because we don’t need to maintain a particular table, to

insert or update the data in that table, and so on. Our backups become smaller and

faster because we don’t store unnecessary data. That is exactly what has happened

with SAP S/4HANA: functional areas that had 37 tables, for example, now have 2 or

3 tables remaining.

To get an idea of the compound effect that SAP HANA has on the system, Figure 3.5

shows how a system shrinks when you start applying this new approach.

Figure 3.5 How a Database Shrinks with SAP HANA In-Memory Techniques

Say that we start with an original database of 750 GB. Because of column-based

storage, compression, and the elimination of indices, the database size easily

comes down to 150 GB. When we then apply the techniques we just discussed for

750 GB

150 GB

50 GB

10 GB

Original data volume

Application migrated to SAP HANA

Application optimized for SAP HANA

Data aging implemented


calculating aggregates instead of storing them in separate tables, the database

goes down to about 50 GB. When we finally apply data aging, the database can

shrink to an impressive 10 GB. In addition, we’re left with a simpler system, less

code, faster reporting response times, and smaller backups.

Delta Buffer and Delta Merge

Before we start sounding like overeager sales people, let’s look at something that

column-based databases aren’t good at. Row-based databases are good at reading,

inserting, and updating single records. Row-based databases aren’t efficient when

we have to add new columns (fields).

When we look at column-based databases, we find these two sentences are

reversed because data stored in columns instead of in rows is effectively “rotated”

by 90 degrees. Adding new columns in a column-based database is easy and

straightforward, but adding new records can be a problem. In short, column-based

databases tend to be very good at reading data, whereas row-based databases are

better at writing data.

Fortunately, SAP took a pragmatic rather than a purist approach and implemented

both column-based and row-based storage in SAP HANA. This allows users to have

the best of both worlds: column storage for compressed data and fast reading, and

row storage for fast writing of data. Because reading data happens more often than

writing, the column-based store is obviously preferred.

Figure 3.6 shows how this is implemented in SAP HANA table data storage. The

main table area is using the column store as we just discussed, and the data is opti-

mized for fast reading.

Next to the main table area, we also have an area called the delta buffer, which is

sometimes called the delta store. When SAP HANA needs to insert new records, it

inserts them into this area. This is based on the row store and is optimized for fast

data writing. Records are appended at the end of the delta buffer. The data in this

delta buffer is therefore normally unsorted and uncompressed.

Some people think the read queries are slowed down when the system has to read

both areas (for old compressed and newly inserted data) to answer a query.

Remember that everything in SAP HANA runs in parallel and uses all available CPU

cores. For the query shown in Figure 3.6, 100 CPU cores can read the main table

area, and another 20 CPU cores can read the buffer area. The data processed by all

120 cores is then combine to produce the result set.


Figure 3.6 Column and Row Storage Used to Achieve Fast Reading and Writing of Data

We’ve looked at cases in which new data is inserted, but what about cases in which

data needs to be updated? Column-based databases can also be relatively slow

during updates. Therefore, in such a case, SAP HANA marks the old record in the

main storage as “updated at date and time” and inserts the new values of the

records in the delta buffer. The SAP HANA system effectively changes an update

into a flag and insert. This is called the insert-only technique.

We don’t want the delta buffer to become too large because a large unsorted and

uncompressed area could slow down query processing. When the delta buffer

becomes too full or when you trigger it, the delta merge process takes the values of

the delta buffer and merges them into the main table area. SAP HANA then starts

with a clean delta buffer.

Column-based databases also don’t handle SELECT * SQL statements well, meaning

reading all the columns. We’ll look at this in more detail in Chapter 12. For now, we

encourage you to avoid such statements in column-based databases by asking

only for the fields (columns) you need.

In the next section, we’ll look at how the SAP HANA in-memory database persists

its data, even when we switch off the machine.

SAP HANA

Reading dataWriting data

Main

Delta buffer

Memory

• Column store• Read-optimized

• Row store• Write-optimized

Delta merge


Persistence Layer

The most frequently asked question about SAP HANA and its in-memory concept

is as follows: “Because memory is volatile and loses its contents when you switch

off its power source, what happens to SAP HANA’s data when you lose power or

switch off the machine?”

The short answer is that SAP HANA still uses disks. However, whereas other data-

bases are disk-based and optionally load data into memory, SAP HANA is in-mem-

ory and merely uses disks to back up the data for when the power is switched off.

The focus is totally different.

The disk storage and the data storage happens in the persistence layer, and storing

data on disk is called persistence.

If hardware fails, having the data also stored in the persistence layer allows for full

recovery. Newer nonvolatile memory, which doesn’t lose its contents when the

power gets switched off, influences the architecture of SAP HANA’s persistence

layer going forward.

SPS 04

As of SAP HANA 2.0 SPS 04, you can use Intel Optane DC persistent (nonvolatile) memory

instead of DRAM in 50% to 80% of your SAP HANA server’s memory slots. SAP HANA is the

first database to use this new type of memory in nonvolatile App Direct Mode. Depend-

ing on the size of the database, it cuts the startup time down to 8% of the usual startup

time on the same hardware.

This type of memory is a bit slower than normal DRAM, but due to the way that SAP

HANA uses data, observed real-life results show performance very close to that of DRAM.

An added benefit is that these memory modules are larger than the DRAM configura-

tions, so you can, for example, put more memory into a four-socket server than you nor-

mally would.

SAP HANA SPS 04 also has a fast restart option that you can use when patching and

restarting SAP HANA. This won’t work when upgrading or rebooting a server. However,

persistent memory (PMEM) does work for such scenarios.

Let’s look at the various ways SAP HANA uses disks and the persistence layer in the

following sections.

SAP HANA as a Distributed Database

Just like many newer databases, SAP HANA can be deployed as a distributed data-

base. One of the advantages that SAP HANA has due to its in-memory architecture


is that it’s an atomicity, consistency, isolation, and durability (ACID)-compliant data-

base. ACID ensures that database transactions are reliably processed. Most other

distributed databases are disk-based, with the associated performance limitations,

and most of them—most notably NoSQL databases—aren’t ACID-compliant.

NoSQL databases mostly use eventual consistency, meaning that they can’t guaran-

tee consistent answers to database queries. They might come back with different

answers to the same query on the same data. SAP HANA will always give consistent

answers to database queries, which is vital for financial and business systems.

Figure 3.7 shows an instance in which SAP HANA is running on three servers. A

fourth server can be added to facilitate high availability. Should any one of the

three active servers fail, the fourth standby server will instantly take over the func-

tions of the failed server.

As a distributed database, SAP HANA’s shared disk storage is used to facilitate high

availability in what is called the scale-out solution.

Figure 3.7 SAP HANA as a Distributed Database

Partitioning

SAP HANA can partition tables across active servers, meaning that we can take a

table and split it to run on all three servers in Figure 3.7. This way, we not only use

multiple CPU cores of one server with SAP HANA to execute queries in parallel

Server 1(active)

Server 4(standby)

Server 2(active)

Server 3(active)

SAP HANA

(Distributed DB,ACID compliant)

Common storage

Table partition 1 Table partition 2 Table partition 3

Failover

Highavailability


but also use multiple servers. We can partition data in various ways; for example,

people whose surnames start with the letters A–M can be in one partition and N–

Z names can be in another.

SAP HANA supports three types of table partitioning, namely round-robin, hash,

and range partitioning. We can use single-level or multilevel partitioning.

Data Volume and Data Backups

Figure 3.8 shows the various ways that SAP HANA uses disks. At the top left, you

can see that the SAP HANA database is in memory. If it’s not in memory, it’s not a

database. The primary storage for transactions is the memory. The disk is used for

secondary storage—to help when starting up after a power down or hardware fail-

ure, with backups, and with disaster recovery.

Figure 3.8 Persistence of Data with SAP HANA

System tables

SAP HANA

Page manager Transaction manager

Log volume

Data blocks (pages)

Data volume

Data backup Log backup

Row store

Column store

Memory

SAP HANAdynamic data tiering

Warm data (on disk)

Hot data(in memory)

SAP HANAdisaster recovery system

� System or storage replication

� Sync or async replication

� Dedicated or shared servers

Async SyncSavepoint At commit/buffer


Data is stored as blocks (also sometimes called pages) in memory. Every 10 min-

utes, a process called a savepoint runs that synchronizes these memory blocks to

disks in the data volume. Only the blocks that contain data that have changed in

the past 10 minutes are written to the data volume. This process is controlled by

the page manager. The savepoint process runs asynchronously in the background.

Data backups are made from the disks in the data volume. When you request a

backup, SAP HANA first runs a savepoint and ensures that the data in the data vol-

ume is consistent, and then starts the backup process.

Note

The backup reads the data from the disks (secondary storage) in the data volume. This

doesn’t affect the database operations because the SAP HANA database continues work-

ing from memory (primary storage). Therefore, you don’t have to wait for an after-hours

time window to start a backup.

SAP HANA can unload (drop) data blocks from memory when it needs more mem-

ory for certain operations. This unloading of data from memory isn’t a problem

because SAP HANA knows that all the data is also safely stored in the data volume.

It will unload the least-used portions of the data. It doesn’t unload the entire least-

used table, but only the columns or even portions of a column that weren’t used

recently. In a scale-out solution, it can even unload parts of a table partition. The

most it might have to do is trigger a savepoint before unloading the least-used

data. The persistence layer is therefore a very useful feature of SAP HANA.

Log Volume and Log Backups

In the middle section of Figure 3.8, shown previously, you can see the transaction

manager writing to the log volume. The page manager and transaction manager

are subprocesses of the SAP HANA index server process, which is the main SAP

HANA process. If this process stops, then SAP HANA stops.

When you edit data in SAP HANA, the transaction manager manages these

changes. All changes are stored in a log buffer in memory. When you COMMIT the

transaction, the transaction manager doesn’t commit the transaction to memory

until it’s also committed to the disks in the log volume, so this is a synchronous

operation. Because the system has to wait for a reply from a disk in this case, fast

SSDs are used in the log volume. This synchronous writing operation is also trig-

gered if the log buffer becomes full.


The log files in the log volume are backed up regularly using scheduled log back-

ups.

Note

SAP HANA doesn’t lose transactions, even if a power failure occurs, because it doesn’t

commit a transaction to the in-memory database unless it’s also written to the disks in

the log volume.

Startup

The data in the data volume isn’t a “database.” Instead, think of it as an organized

and controlled memory dump. If it was a database on disk, it would have to convert

the disk-based database format to an in-memory format each time it started up.

Now, it can just reload the “memory dump” from disk back into memory as

quickly as possible.

When you start up an SAP HANA system, it performs what is called a lazy load. It

first loads the system tables into memory and then the row store. Finally, it loads

any column tables that you’ve specified. Because the data volumes are only

updated by the savepoint every 10 minutes, some transactions might not be in the

data volume; SAP HANA loads those transactions from the log volume. Any trans-

action that was committed stays committed, and any transaction that wasn’t com-

mitted is rolled back. This ensures a consistent database.

SAP HANA then reports that it’s ready for work. This ensures that SAP HANA sys-

tems can start up quickly. Any other data that is required for queries gets loaded in

the background. Again, SAP HANA doesn’t have to load an entire table into mem-

ory; it can load only certain columns or part of a column into memory. In a scale-

out system, it will load only the required partitions.

Disks are therefore useful for getting the data back into memory after system

maintenance or potential hardware failures.

Disaster Recovery

At the bottom right of Figure 3.8, shown earlier, you can see that the data volume

and logs also are used to facilitate disaster recovery. In disaster recovery, you make

a copy of the SAP HANA system in another data center. If your data center is

flooded or damaged in an earthquake, your business can continue working from

the disaster recovery copy.


The initial large copy of the system can be performed by replicating the data vol-

ume. After the initial copy is made, only the changes are replicated as they are writ-

ten to the log volume. This is known as log replication.

This replication can be performed by the SAP HANA system itself or by the storage

vendor of the disks in the data and log volumes. The replication can happen either

synchronously or asynchronously. You can also choose whether to use dedicated

hardware for the disaster recovery copy or shared hardware by using the disaster

recovery machines for development and quality assurance purposes.

Note

Disaster recovery is covered in more detail in the HA200 course, and in the SAP HANA

administration guide at http://s-prs.co/v487620.

Active/Active (Read-Enabled) Mode

SAP HANA 2.0 has a powerful new feature called active/active (read-enabled) mode

that allows you to actively use the disaster recovery hardware to help carry the

load on a production system and enhance performance.

This feature only works when you’ve dedicated hardware for the disaster recovery

system. After the disaster recovery system (also known as the secondary system)

is synchronized with the primary SAP HANA system, only the updates need to be

sent across via log replication, as shown in Figure 3.9. The changes are then

replayed asynchronously on the secondary system.

Figure 3.9 SAP HANA Active/Active (Read-Enabled) Mode

Because both systems are in sync and contain the same data, we can now use the

secondary system for reporting and other read-only requirements. Updates must

SAP HANA(secondary)

Disaster recoverysystem

SAP HANA(primary)

Log replication

Application

Read and write Read-only

Log replayedasynchronously



happen on the primary system because the direction of the log replication is only

from the primary to the secondary system. Any actions that insert, edit, update, or

delete data therefore need to use the primary system. All other actions can use

either the primary or the secondary system. The load is thus spread across both

systems. This feature means you can buy smaller machines because you can use

the dedicated disaster recovery hardware to help with your productive load, lead-

ing to cost savings.

Previously, we mentioned how reporting users could affect the performance of

operational systems of old disk-based systems. The solution was to separate OLTP

and OLAP into different applications. With SAP HANA, we run both together. Using

the active/active (read-enabled) mode, we can still separate read-intense opera-

tions, such as reporting or data analysis, using the secondary system. But this is

now still running on the same application based on SAP HANA instead of a sepa-

rate data warehouse.

SPS 04

With SAP HANA SPS 04, you can now create multiple secondary sites using multi-target

replication. This can be used, for example, to have multiple secondary sites for regions to

decrease latency for end users.

Data Aging

The final piece of the persistence layer is found at the top right of Figure 3.8, shown

earlier. This is where disks are used for data aging.

Data aging is normally described by the speed at which the data can be accessed.

Data in memory can be accessed very quickly, so this is referred to as “hot” data.

Data on disk is a bit slower, so this is called “warm” data. Data in other storage, such

as an SAP IQ database or a Hadoop data lake, is called “cold” data. The temperature

of the data refers to how fast data can be accessed, with hot having the fastest

access. As the speed of data access decreases, the price of the storage also normally

decreases.

The idea behind data aging is that we want the latest data, that is, the data used

most often, in the hot storage area. As the data becomes older, we don’t need it as

often, and it can be moved to the warm storage area. After a while, we’ll probably

never use the data again, but we might need to keep it due to regulatory require-

ments. In that case, we can move it to the cold storage area. You can specify data

aging rules inside SAP HANA to take, for example, all data older than five years and


write it to the next storage area. All queries to the data still work perfectly but are

just slower because they are read from disk instead of memory.

The warm storage area is managed as a unified part of the SAP HANA database.

This is in contrast to the cold storage area, which is managed separately, although

the data is still accessible at any time. With SAP HANA, there are three options for

warm data, as follows:

� Native storage extension (NSE)

This is the primary warm storage option for SAP HANA. It’s an intelligent built-

in extension that supports all SAP HANA data types and models. NSE allows for

a simple system landscape and is scalable with good performance.

� Extension node

This option allows for better performance than NSE.

� Dynamic tiering

This option allows for a larger data volume than NSE.

You shouldn’t mix multiple warm store options in one landscape for complexity

reasons.

In SAP HANA 2.0, we can use the extension node warm store using multistore

tables that are partitioned across both the in-memory data store and the extended

storage on disk, as shown in Figure 3.10. The partitions with the most recent data

can be in memory, and the remaining partitions can be in the extended storage.

SPS 04

SAP HANA extension node benefits from new partitioning and scale-out features in SAP

HANA SPS 04. Some of the new features are partition grouping and a range-hash parti-

tion scheme.

Figure 3.10 Data Aging Using Multistore Tables

You’ll need extended storage installed before you can use multistore tables.

Extended storage

SAP HANA

Table partition 1 Table partition 2

Memory storage

Multistore table


Tip

Multistore tables are only available in SAP HANA 2.0.

Deployment Scenarios

People sometimes get confused about SAP HANA when it’s only defined with a sin-

gle concept, such as SAP HANA is a database. After a while, they hear seemingly

conflicting information that doesn’t fit into that simple description. In this sec-

tion, we’ll look at the various ways that SAP HANA can be used and deployed: as a

database, as an accelerator, as a platform, as a data warehouse, or in the cloud.

When looking at SAP HANA’s in-memory technology, and especially the fact that

we should ideally move code away from the application servers into SAP HANA, it

became clear that SAP systems needed to be rewritten completely.

Because SAP HANA doesn’t need to store values such as year-to-date, aggregates,

or balances, we should ideally remove them from the database. By doing so, we can

get rid of a significant percentage of the tables in, for example, a financial system.

However, that implies that we should also trim the code that calculated those

aggregates and inserted them into the tables we removed. In this way, we can also

remove a significant percentage of our application code.

Next, the slow parts of the remaining code that manipulate data should be moved

into graphical calculation views in SAP HANA, removing even more code from our

application. If we follow this logic, we obviously have to completely rewrite our

systems. This wasn’t easy for SAP, which has millions of lines of code in every sys-

tem and an established client base. This approach can be very disruptive. Disrup-

tive innovation is great when you’re disrupting other companies’ markets, but not

so much when you disrupt your own markets. Ask an existing SAP customer if he

wants this new technology, and he probably will say, “Yes, but not if it means a

reimplementation that will require lots of time, money, people, and effort.” Com-

pletely rewriting the system would lead to customers replacing their systems with

the new solution; that also means that customers with the older systems would

not benefit at all from this innovation.

If you look at the new SAP S/4HANA systems, you’ll see that they follow the more

disruptive approach we just described. SAP has also stated that its business appli-

cations will be migrated to run on SAP HANA. In most cases, they will just run on

SAP HANA, as these systems will be optimized to make full use of SAP HANA’s

capabilities.


However, there are other ways in which we can implement this disruptive new

technology in a nondisruptive way—and this is how SAP approached the process

when implementing SAP HANA a few years ago. The following sections walk

through the deployment scenarios for implementation.

SAP HANA as a Side-by-Side Accelerator Solution

The first SAP HANA deployment scenario is a side-by-side accelerator solution, as

illustrated in Figure 3.11.

Figure 3.11 SAP HANA Deployed as a Nondisruptive Side-by-Side Accelerator Solution

In this deployment scenario, the source systems stay as they are. No changes are

required. We add SAP HANA on the side to accelerate only those transactions or

reports that have performance issues in the source systems, and then we duplicate

the relevant data from the source system to SAP HANA. This duplication is nor-

mally performed by the SAP Landscape Transformation Replication Server. (We’ll

discuss this topic in Chapter 9.) The copied data in SAP HANA is then used for

analysis or reporting.

SAP HANA

Analytics andReporting

Innovation without disruption

Non-SAP

Database

SAP ERP

Database

SAP CRM

Database

SLTReplication

Server


The advantages of this deployment option include the following:

� Because we just copy data, we don’t change the source system at all. Therefore,

everyone can use this innovation without the disruption (even those who have

very old SAP systems).

� We can use this option for all types of systems, not just the latest SAP systems.

In fact, a large percentage of the systems that use SAP HANA are non-SAP sys-

tems.

� We don’t have to copy the data for an entire system. If a report already runs in

three seconds, that’s good enough; it doesn’t have to run in three milliseconds.

If a report runs for two hours, but runs only once a year and is scheduled to run

overnight, that’s fine too. We only take the data into SAP HANA that we need to

accelerate.

� We can replicate data from a source system in real time. The reports running on

SAP HANA are always fully up to date.

Even though we make a copy of data, remember that SAP HANA compresses this

data and doesn’t copy all the data. Therefore, the SAP HANA system is much

smaller than the source systems in this deployment scenario.

However, the SAP HANA system in the scenario we described starts as a blank sys-

tem. You’ll have to load the data into SAP HANA, build your own information

models, link them to your reports and programs, and look after the security. Much

of what we’ll discuss in this book is relevant for this type of work.

Using SAP HANA in the side-by-side deployment involves some work because it

starts as a blank system. SAP quickly realized that special use cases occurred at

many of their customers where some common processes were slow; one of these

processes is profitability analysis. Profitability is something that any company

needs to focus on, but profitably analysis reports are slow running due to the large

amounts of data they have to process. By loading profitability data into SAP HANA,

the response times of these reports go from hours to mere seconds (see Figure

3.12).

For these accelerator cases, SAP provides prebuilt models in SAP HANA. SAP also

slightly modified the ABAP code for Profitability Analysis (CO-PA) to read the data

from SAP HANA instead of the original database. SAP Landscape Transformation

Replication Server replicates just the required profitability data from the SAP ERP

system to the SAP HANA system.


Figure 3.12 SAP HANA Used as an Accelerator for a Current SAP Solution

The installation and configuration process is performed at a customer site over a

few days, instead of weeks and months. The only thing that changes for the end

users is that they have to get used to the increased processing speed, and no one

has a problem with that!

The SAP HANA Live topic that we’ll discuss in Chapter 14 can also be used in a side-

by-side configuration.

SAP HANA as a Database

When SAP released the side-by-side solution, many people thought that it would

replace their data warehouses. However, this worry was alleviated when SAP

announced that SAP HANA would be used as the database under SAP Business

Warehouse (SAP BW). Figure 3.13 illustrates a typical (older) SAP landscape.

SAP HANA can be used to replace the databases under the older SAP BW, SAP Cus-

tomer Relationship Management (SAP CRM), and SAP ERP (which are part of SAP

Business Suite) systems. There are well-established database migration proce-

dures to replace a current database with SAP HANA. In each case, SAP HANA

replaces the database only. The SAP systems still use the SAP application servers as

they did previously, and end users still access the SAP systems as they always did,

via the SAP application servers. All reports, security, and transports are performed

as before. Only the system administrators and DBAs have to learn SAP HANA and

log in to the SAP HANA systems.

SAP ERP

Database

SAP HANACO-PAABAP

SLTReplication

Server


Figure 3.13 Typical Current SAP Landscape Example

When you use SAP HANA as a method to just accelerate reporting and analytics,

you get a similar situation. The users log in to the reporting and analytics tools,

and they basically use SAP HANA as a database.

Figure 3.14 looks very similar to Figure 3.13, except that the various databases were

replaced by SAP HANA.

By only replacing the database, SAP again managed to provide most of the benefit

of this new in-memory technology to businesses but without the cost, time, and

effort of disruptive system replacements. The applications’ code wasn’t forked to

produce different code for SAP BW on SAP HANA versus SAP BW on older data-

bases.

Over time, after customers and partners became familiar with SAP HANA, SAP

moved these solutions completely over to SAP HANA. We now have SAP S/4HANA

and SAP BW/4HANA that replace the SAP ERP and SAP BW systems, respectively,

that just used SAP HANA as yet another database.

Enterprise datawarehouse(SAP BW)

Corporatereporting and

analytics

Database

Non-SAP

Database

SAP ERP

Database

SAP CRM

Database


Figure 3.14 SAP HANA as Database Can Change the Current Landscape

Let’s look at the example of SAP BW in a bit more detail. Because SAP HANA can

address both OLAP and OLTP in a single system, a data warehousing solution such

as SAP BW has changed a lot. SAP started with the “classic” SAP BW that was run-

ning on other databases. When SAP HANA was released, SAP BW was ported to also

run on SAP HANA, and it used SAP HANA as just another database. The application

stayed roughly the same.

Remember that the “classic” SAP BW has its own information modeling objects—

for example, InfoCubes and DataStore Objects (DSOs). These objects in SAP BW are

built in the application server layer. Even if SAP BW is powered by SAP HANA, and

these SAP BW objects are optimized for SAP HANA, they are still completely differ-

ent from the normal SAP HANA information models, such as calculation views.

As SAP HANA became more popular, and people started realizing the advantages

of using SAP HANA, new features started appearing in SAP BW. With SAP BW 7.4,

Enterprise datawarehouse(SAP BW)

Corporatereporting and

analytics

SAP HANA

Non-SAP

Database

SAP ERP

SAP HANA

SAP CRM

SAP HANA


we had SAP BW powered by SAP HANA. We could use the classical SAP BW model-

ing objects alongside some new SAP HANA-specific ones such as Advanced Data-

Store Objects (Advanced DSOs). It could also run both the old 3.x and the new 7.x

data flows. SAP HANA enhanced the performance of SAP BW significantly.

SAP then rewrote its SAP Business Suite systems to make full use of SAP HANA.

In the process, the systems became a lot simpler and smaller. This is the SAP

S/4HANA system.

Looking at SAP BW, SAP decided to do the same for SAP BW by creating a new ver-

sion of SAP BW, called SAP BW/4HANA, that relies solely on SAP HANA. It doesn’t

run on any other database, like the “classic” SAP BW did.

In the process, SAP also simplified and reduced the size of SAP BW but with even

more capabilities. Table 3.2 shows how the number of modeling objects was

reduced from 10 to just 4. For example, the InfoCubes and DSOs we mentioned ear-

lier were both replaced with Advanced DSOs. In SAP BW/4HANA, these old objects

aren’t available. Old functionality such as 3.x data flows are also no longer avail-

able.

SAP HANA as a Platform

The next way we can deploy SAP HANA is by using it as a full development plat-

form. SAP HANA is more than just an in-memory database. We’ll quickly discuss

how SAP HANA 1.0 evolved into a development platform.

Classic SAP BW SAP BW/4HANA

� MultiProvider

� InfoSet

� CompositeProvider

� Virtual Provider

� CompositeProvider

� Virtual Provider

� Transient Provider

� Open ODS view

� InfoCube

� DSO

� Hybrid Provider

� Persistent Staging Area (PSA) Table

� Advanced DSO

� InfoObject � InfoObject

Table 3.2 Reduced Number of Modeling Object Types in SAP BW/4HANA


Which other database has several different programming languages included and

has version control, code repositories, and a full application server built in? SAP

HANA 1.0 already had all of these. This makes SAP HANA 1.0 not just a database,

but a platform. You can program in SQL, SQLScript (an SAP HANA-only enhance-

ment that we’ll look at in Chapter 8), R, and JavaScript.

However, the main feature we use inside SAP HANA 1.0 when deploying it as a plat-

form is the application server, called the SAP HANA extended application services

(SAP HANA XS) engine, which is used via web services (see Figure 3.15). A new ver-

sion of the SAP HANA XS engine appeared in the last support packs of SAP HANA

1.0, called SAP HANA extended application services, advanced model (SAP HANA

XSA). The older version we’re discussing here was renamed to SAP HANA extended

application services, classic model (SAP HANA XS). The SAP HANA XS engine is a

full web and application server built into SAP HANA 1.0 that is capable of serving

and consuming web pages and web services.

Figure 3.15 SAP HANA 1.0 as a Development Platform

When we deploy SAP HANA 1.0 as a platform, we only need an HTML5 browser and

SAP HANA XS. In this case, we still develop data models as we discussed previously

with the side-by-side accelerator deployment, but we also develop a full applica-

tion in SAP HANA using JavaScript and an HTML framework called SAPUI5. All this

functionality is built in to SAP HANA 1.0 and SAP HANA XS.

HTML5 browser

Mobile(tablet or

phone)

Model-View-Controller (MVC)

SAP HANA

SAP HANA viewsSAP HANA views• SAP HANA views

• SQL, SQLScript

Model

Extended ApplicationServices (XS engine)

• OData

• REST

• Server-side JavaScript

ControllerView displayed

View createdusing SAPUI5

• HTML5• CSS3• Client-side JavaScript


The web application development paradigm that many frameworks use is called

model-view-controller (MVC). Model in this case refers to the information models

that we’ll build in SAP HANA and is the focus of this book. We expose these infor-

mation models in the controller as OData or REST web services. This functionality

is available in SAP HANA and is easy to use. You can enhance these web services

using server-side JavaScript, meaning that SAP HANA runs JavaScript as a program-

ming language inside the server.

The last piece is the view, which is created using the SAPUI5 framework. The

screens are the same as the SAP Fiori screens in the new SAP S/4HANA systems,

which are HTML5 screens. What is popularly referred to as HTML5 actually consists

of HTML5, Cascading Style Sheets (CSS3), and client-side JavaScript. In this case, the

JavaScript runs in the end user’s browser.

By using JavaScript for both the view and the controller, SAP HANA XS developers

only have to learn one programming language to create powerful web applications

and solutions that leverage the power and performance of SAP HANA.

We can even create native mobile applications for Apple iOS, Android, and other

mobile platforms. Some free applications, such as Cordova, take the SAP HANA

web applications and compile them into native mobile applications, which can

then be distributed via mobile application stores. As a result, you can use SAP

HANA’s power directly from your mobile phone. You can also use the SAPUI5

application with the React framework and React Native to create native mobile

applications.

In the next chapter, we’ll see how these concepts expanded with SAP HANA XSA

and SAP HANA 2.0.

SAP HANA as an SQL Data Warehouse

A relatively new use case of SAP HANA is as an enterprise data warehouse (EDW).

SAP has offered the SAP BW system for customer’s data warehousing needs for

many years. SAP BW has evolved to use SAP HANA and has been optimized for SAP

HANA with SAP BW/4HANA. It’s still the recommended route for customers who

want strong built-in features for data governance and integrity, processes, con-

trols, and integration to other SAP systems for an enterprise-wide solution. How-

ever, more and more companies want to build their own data warehouse using

only SAP HANA.

Whereas SAP BW is a data warehousing application using SAP HANA, the new SAP

SQL Data Warehousing approach is a developer-oriented and SQL-driven approach


to data warehousing using SAP HANA XS. In both cases, you can provision data

using the SAP tools (discussed in Chapter 9) and using the SAP BusinessObjects

tools or SAP Analytics Cloud (discussed later in this chapter).

SAP HANA has provided strong support for the OLAP warehousing functionality

for many years now. You’ll see this in the next few chapters. SAP has now added an

optional component called the SAP HANA data warehousing foundation for opti-

mized data distribution, lifecycle management, monitoring, and scheduling. This

additional functionality helps companies build native data warehouses using just

SAP HANA.

Note

SAP HANA data warehousing foundation starts with a blank system and offers a do-it-

yourself approach to build your own data warehouse using loosely coupled tools that

may have separate repositories.

Figure 3.16 shows the high-level architecture of the SAP SQL Data Warehousing

with SAP HANA data warehousing foundation version 2.0. At the bottom are the

various data sources of the data provisioned to SAP HANA, which we’ll cover in

Chapter 9.

The middle of the figure shows SAP SQL Data Warehousing, and we’ll focus on

most of the modeling and development functionality shown here in this book.

The left side of the figure shows the various tools we’ll use in this book for building

what is required in the data warehouse. The right side of the figure shows the vari-

ous data lake options that we can exchange data with.

Finally, the reporting and analysis layer appears at the top, as discussed later in

this chapter.

Tip

The versions of SAP HANA data warehousing foundation are independent from the ver-

sions of SAP HANA. It’s important to note that SAP HANA data warehousing foundation

1.0 used SAP HANA XS, whereas the current SAP HANA data warehousing foundation 2.0

uses SAP HANA XSA.

Although Figure 3.16 shows SAP HANA data warehousing foundation 2.0, we’ll also

quickly discuss SAP HANA data warehousing foundation 1.0.


Figure 3.16 SAP SQL Data Warehousing with SAP HANA Data Warehousing Foundation 2.0

SAP HANA data warehousing foundation 1.0 delivers several SAP HANA XS appli-

cations:

� Data Distribution Optimizer (DDO)

This application is used with SAP HANA scale-out systems to analyze, plan, and

adjust the reorganizations of data in the landscape. Tables with data that are

used together often can be moved to the same node to enhance query perfor-

mance.

� Data Lifecycle Manager (DLM)

This application enables data distribution and aging using, for example,

dynamic data tiering.

� Data Warehouse Monitor (DWM)

This application shows the activities in the data warehouse.

� Data Warehouse Scheduler (DWS)

This application provides, for example, a framework to define task chains as a

sequence of single tasks.

Data Sources SAP S/4HANA Twitter Sensors Other DBs Teradata SAP Fieldglass …

Provisioning ETL Streaming Replication Virtual Access

Consume SAP Lumira BI Predictive SAPUI5 Third-Party SAP BusinessObjects …

Tools

SAP Web IDE forSAP HANA Calculation Views

SAP Vora

Data Lake

GitHub

…

Hadoop

Altiscale

Spark

…

SAP EnterpriseArchitecture

Designer

SAP HANA SQL Data Warehousing

In-Memory

SQL Procedures

TaskChain CDS – NDSO DW Monitor

Flowgraphs Virtual Tables


The last two options were shown as planned functionality in SAP HANA 1.0, but

they are available in SAP HANA 2.0.

You might ask when you would use SAP SQL Data Warehousing. If you want to

build your own data warehousing as part of an application you’re building, you

prefer a web user experience such as a 100% open SQL approach, you want the

freedom to create custom data models and processes, or you want to use tech-

niques such as DevOps and continuous integration, testing, and deployment, this

is definitely something you should look at.

SAP HANA in the Cloud

SAP HANA has many features that make it ideal for use in a cloud platform envi-

ronment. SAP, like many other enterprises, has moved aggressively into the cloud

with many of its solutions. Many features that are required for smooth cloud oper-

ations were built into SAP HANA over the past few years.

SAP HANA works great as a cloud enabler because it accelerates applications, pro-

viding users with fast response times, which we all expect from such environ-

ments.

Multitenant Database Containers

The multitenant database containers (MDC) deployment option became available

as of SAP HANA 1.0 SPS 09 and helps us move from the world where everything is

deployed in the company’s data center, called on premise, to the cloud. We can now

have multiple isolated databases inside a single SAP HANA system, as shown in

Figure 3.17. Each of these isolated databases is referred to as a tenant.

Think of tenants occupying an apartment building in which each tenant has a

front door key and owns furniture inside his or her apartment. Other tenants can’t

get into another tenant’s apartment without permission. However, the mainte-

nance of the entire apartment building is performed by a single team. If the build-

ing needs repairs to the water pipes, all tenants will be without water for a while.

In the same way, tenants in MDC have their own unique business systems, data,

and business users. In Figure 3.17, you can see that each of the three tenants has its

own unique applications installed and connected to its own tenant database

inside SAP HANA.


Figure 3.17 Multitenancy with SAP HANA MDC

Each tenant can make its own backups if the user wishes. Tenants can also collab-

orate, so one tenant can use the SAP HANA models of another tenant if the tenant

has been given the right security access.

However, all the tenants share the same technical server infrastructure and main-

tenance. The system administration team can make a backup for all the tenants.

(Any tenant can be restored individually, though.) SAP HANA upgrades and main-

tenance schedules happen at the same time for all the tenants.

Your own private SAP HANA system can be deployed using MDC. Even the free SAP

HANA, express edition, which we discussed in Chapter 2, uses the MDC deploy-

ment option. But MDC makes the most sense in a cloud environment.

Multitenancy is a vital part of any cloud strategy. When you sign up for a cloud

account, you don’t want to wait several weeks for hardware to be procured. You

expect the system to be available in a few minutes. Creating new tenants allows

this to happen as expected, while maintaining full isolation from other tenants’

information.

Note

The tenants are all hosted inside a single SAP HANA system, which means that all tenants

use the same version of SAP HANA. When the SAP HANA system gets upgraded or

patched, this affects all the tenants.

Application 1SAP HANA Tenant 1(own data, own users)

Tenant 2(own data, own users) Application 2

BrowserTenant 3(own data, etc.) XS

Common Administration

Upgrades

Backups

Monitoring

Server maintenance


Cloud Deployments

We’ve now discussed various ways to deploy SAP HANA. Most of the time, we

think of these deployments as running on premise on a local server, but all of

these scenarios can also be deployed in the cloud. In cloud deployments, virtual-

izations manage applications such as SAP HANA. VMware vSphere virtual

machine deployments of SAP HANA are fully supported, both for development

and production instances.

With cloud deployments, you’ll be introduced to various new terms:

� Infrastructure as a service (IaaS)

An infrastructure includes things such as network, memory, CPU, and storage.

IaaS provides infrastructure in the cloud to support your deployments.

� Platform as a service (PaaS)

PaaS uses solutions such as SAP Cloud Platform. In this scenario, you use not

only the same infrastructure as IaaS but also a platform, such as a Java applica-

tion service or the SAP HANA platform services.

� Software as a service (SaaS)

This cloud offering provides certain software, such as SAP SuccessFactors or SAP

C/4HANA.

� Managed cloud as a service (McaaS)

In McaaS, SAP manages the entire environment for you, including backups,

disaster recovery, high availability, patches, and upgrades. An example of this is

the SAP HANA Enterprise Cloud.

Public, Private, and Hybrid Cloud

Your cloud scenario could be public, private, or hybrid. Let’s look at what each of

those terms means and when each scenario applies.

The easiest and cheapest way for you to get an instance of SAP HANA for use with

this book is via Amazon Web Services (AWS), Google Cloud Platform (GCP), or

Microsoft Azure. We’ve looked at this in Chapter 2. By using your own account, you

can have an SAP HANA system up and running in minutes. This is available to

everyone—the public—so therefore this is a public cloud. In this example, the

cloud provider provides IaaS.

The reason this is called a public cloud is that the infrastructure is shared by mem-

bers of the public. You don’t get your own dedicated machine at Amazon, Micro-

soft, Google, or any other public cloud provider.


If you host SAP HANA in your own data center or in a hosted environment specif-

ically for you only, then it’s a private or hybrid cloud. A private cloud offers the

same features that you would find in a public cloud, but it’s dictated by different

forms of access. With a private cloud, the servers in the cloud provider’s data cen-

ter are dedicated for your company only. No other companies’ programs or data

are allowed on these servers.

A hybrid cloud involves either combining a public and private cloud offering, or

running a private and public cloud together. With a hybrid cloud, you connect

your private cloud’s server back to your own data center via a secure network con-

nection. In that case, the servers in the cloud act as if they are part of your data cen-

ter, even though they are actually in the cloud provider’s data center. An example

of a hybrid cloud is using a Microsoft Azure cloud and another private cloud

together.

SAP HANA Enterprise Cloud

SAP HANA Enterprise Cloud offers SAP applications as cloud offerings. You can

choose to run SAP HANA, SAP BW/4HANA, SAP S/4HANA, SAP CRM, SAP Business

Suite, and many others as a full service offering in the cloud. All the systems that

you would normally run on premise are available in SAP HANA Enterprise Cloud.

SAP provides and manages the hardware, infrastructure, SAP HANA, and other SAP

systems for the customer. The customer only has to provide the license keys for

these systems. This is referred to as bring your own license.

SAP HANA Enterprise Cloud is seen as an MCaaS solution on a private cloud, but it

can also be deployed in a hybrid cloud offering.

SAP Cloud Platform

SAP Cloud Platform is a PaaS that allows you to build, extend, and run applications

in the cloud. Services include SAPUI5, HTML5, SAP HANA platform services, Java

applications, and more.

SAP Cloud Platform was previously known as SAP HANA Cloud Platform. Behind

the scenes, more than just a name change happened. SAP Cloud Platform not only

uses SAP HANA but also many open-source technologies such as OpenStack and

Cloud Foundry. SAP is a Founding Platinum Level Member of the Cloud Foundry

Foundation, which means that you can use Cloud Foundry buildpacks and Docker


containers, with microservices written in any language of your choice, in SAP

Cloud Platform.

SAP Cloud Platform also offers a wide range of services to get your cloud applica-

tions up and running quickly. These can be, for example, security, HTML frame-

work, database, Internet of Things (IoT), machine learning, predictive, provision-

ing, analytics, mobile, development, build, and deployment services. You can even

sell your SAP Cloud Platform applications in the SAP App Center.

SAP Cloud Platform forms the core of the new SAP Leonardo offering, where SAP

focuses on digital innovation and transformation. SAP Cloud Platform will also

run on the cloud offerings of Amazon, Google, and Microsoft. This new option is

termed multi-cloud.

To better understand SAP Cloud Platform, Cloud Foundry, and the new develop-

ment process, we’ll return to SAP HANA in the next chapter and look deeper into

SAP HANA XSA.

SAP Analytics Cloud and Business Intelligence

One topic that has been heavily influenced by SAP HANA is that of analytics. Run-

ning fast and interactive analytics in the cloud was unforeseen just a few years ago.

Everyone was using on-premise reporting tools, and these reports often took

hours to generate. SAP HANA changed that very quickly by generating the same or

better reports in mere seconds.

Today the focus is on SAP Analytics Cloud, but SAP has a lot of other noncloud BI

tools as well, which you’ll see in the next sections. You can connect all these tools

to SAP HANA; however, all BI tools must be used correctly with SAP HANA to

ensure optimal benefit.

SAP Analytics Cloud

In the space of two or three years, the emphasis moved from running analytics and

reports on your own computer to using SAP Analytics Cloud. One of the main

advantages is that SAP Analytics Cloud gets updated every few weeks, whereas the

on-premise tools often had update cycles of years.

Another advantage is that SAP Analytics Cloud displays everything in your

browser. It doesn’t matter whether you’re using a tablet, a Windows laptop, or an

Apple Mac, you no longer need to install drivers, special programs, and the like.


Figure 3.18 shows an example of the SAP Analytics Cloud functionality in a web

browser. You can easily share your analytics and reports with other users. And

when you compare the results, features, and tools available for creating BI content

in SAP Analytics Cloud, it clearly wins over older reporting tools.

Figure 3.18 An SAP Analytics Cloud Screen in a Web Browser

You can even use it in hybrid scenarios, where you use both on-premise and cloud

data sources. The on-premise data sources can range from Microsoft Excel and

comma-separated values (CSV) data files to SAP BW and universes.

SAP Analytics Cloud gives you the ability to combine BI, available industry con-

tent, planning, analysis, and predictive to create interactive views that also allow

users to drill down to the underlying data. Figure 3.18 shows SAP Digital Board-

room, built on SAP Analytics Cloud, that allows executives to view all their com-

pany metrics in a comprehensive dashboard, while still allowing them to explore

the data underneath each of the graphs or tables. Having data at your fingertips

allows you to make decisions immediately, instead of waiting for additional BI

reports that used to take days or weeks.

SAP Business Intelligence Tools

Along with SAP Analytics Cloud, SAP also has a large number of other BI tools that

it has collected and developed over the years. Some were added when SAP


acquired Business Objects. Others were BI tools developed for use with SAP BW.

Others are new tools developed to make use of SAP HANA or to add new BI fea-

tures, such as visualization and predictive analysis.

SAP also has the powerful SAP BusinessObjects BI platform, where reports, with

their user administration, security, storage, and scheduling, can be centrally man-

aged.

SAP realized that the list of BI tools was too long and decided to converge these

into just five:

� SAP Analysis for Microsoft Office

This tool runs as a Microsoft Office plug-in. It appears in Excel as an additional

ribbon bar menu and provides powerful reporting from trusted OLAP data

sources, such as SAP HANA.

� SAP Lumira, discovery edition

This comparatively new tool for self-service and visualization of analytics was

designed with SAP HANA in mind, so it leverages SAP HANA features to deliver

fast results. SAP Lumira, discovery edition integrates with SAP Analytics Cloud.

It’s also easy to consume SAP Lumira content on mobile devices via the SAP

BusinessObjects Mobile app. SAP Lumira is well suited for ad hoc types of ana-

lytics; you can view and visualize any type of data in the way you like with min-

imal effort.

� SAP Lumira, designer edition

This tool is used to create dashboards, OLAP web applications, and guided ana-

lytics. It features native (direct) connections to SAP BW and SAP HANA, and it

falls into the professionally authored category, in which analytic content is cen-

trally governed. Because it’s more of a developer’s tool, some knowledge of Java-

Script is required to get the most out of the tool. SAP Lumira, designer edition,

has a “what you see is what you get” (WYSIWYG) design environment.

� SAP BusinessObjects Web Intelligence

Popularly called WebI, this is one of the most popular SAP BI tools because it’s

easy to work with, yet powerful and fast enough to get the job done. It was one

of the first self-service style BI tools from SAP. End users can consume SAP intel-

ligence reports using the mobile app or via a browser.

� SAP Crystal Reports

This tool is used to create reports that often have the exact specifications and

look of the existing paper forms in a “pixel-perfect” format. SAP Crystal Reports


focuses on professionally authored reports, which can be viewed on desktops,

browsers, and offline. This tool has been around since the 1990s and was proba-

bly one of the first reporting tools that most modelers and developers used.

Let’s now see how you can use these tools with SAP HANA.

Using Business Intelligence Tools with SAP HANA

One of the most important lessons to learn from the way that SAP Analytics Cloud

works is how it utilizes interactive drilldown analytics. When we use BI tools this

way, people become more productive because SAP HANA is actually being used in

the optimal way.

Let’s use Excel as an example. It’s obviously not an SAP tool but has been the most

widely used and best-known BI tool available for more than 30 years.

Excel is probably the ultimate self-service BI tool. However, despite all its flexibil-

ity, it’s also easy to use it “incorrectly.” If you import 700,000 records into Excel

and then build a pivot table on that data, you’re not using Excel in the best possible

way. If you use it that way, you use Excel as a database—and it’s not meant to be a

database. You’re also straining the network while transferring that much data. You

get users complaining about how slow their reports are, only to discover that they

run them in Excel on old laptops with very little memory on a slow network.

A better way to use Excel is to connect it to SAP HANA information views, as shown

in Figure 3.19 1. You can then build the pivot table directly on the SAP HANA view

2. In this case, SAP HANA performs all the calculations, and Excel just displays the

results. The 700,000 records stay in SAP HANA, and the minimum amount of data

for displaying the results is transferred via the network. SAP HANA usually also

uses less memory and thus is faster.

The best way to test if you’re using BI tools correctly is to ask whether it will work

on a mobile device, such as a tablet. If you load 700,000 records into Excel, it won’t

work on your tablet. The tablet simply doesn’t have enough memory, and your

mobile data costs will be high. With the “correct” way, on the other hand, you

could probably get this setup working on the mobile app version of Excel if you

had the correct driver installed.

An added benefit of letting SAP HANA do the heavy lifting is that your reports and

analytics become interactive; for example, drilldowns are easy to implement.


Figure 3.19 Using SAP HANA Information Models with Hierarchies in an Excel Pivot Table

Connecting Business Intelligence Tools to SAP HANA

SAP HANA provides drivers known as SAP HANA clients that are commonly used

to connect SAP HANA to other types of databases and BI tools or vice versa.

The query language sent over these connections is normally SQL, which we’ll dis-

cuss in Chapter 8. It can also sometimes be multidimensional expressions (MDX).

MDX is a query language for OLAP databases, similar to how SQL is a query lan-

guage for relational OLTP databases. The MDX standard was adopted by a wide

range of OLAP vendors, including SAP.

Let’s look at some of the common database connections you might encounter:

� Open Database Connectivity (ODBC)

ODBC acts as a translation layer between an application and a database via an

ODBC driver. You write your database queries using a standard application


programming interface (API) for accessing database information. The ODBC

driver translates these into database-specific queries, making your database

queries database and operating system independent. By changing the ODBC

driver to that of another database and changing your connection information,

your application will work with another database.

� Java Database Connectivity (JDBC)

JDBC is similar to ODBC, but it’s specifically aimed at the Java programming lan-

guage.

� Object Linking and Embedding Database for Online Analytical

Processing (ODBO)

ODBO provides an API for exchanging metadata and data between an applica-

tion and an OLAP server (e.g., a data warehouse using cubes). You can use ODBO

to connect Excel to SAP HANA.

� Business Intelligence Consumer Services (BICS)

BICS is an SAP-proprietary database connection. This direct client connection

performs better and faster than MDX. Hierarchies are supported, negating the

need for MDX in SAP environments. Because this is an SAP-only connection

type, you can only use it between two SAP systems, for example, from an SAP-

provided BI tool to SAP HANA.

The fastest way to connect to a database is via dedicated database libraries. ODBC

and JDBC insert another layer in the middle that can impact your database query

performance. Some reporting tools offer a choice of connectors, for example,

allowing you to use either a JDBC or an ODBC connector.

Excel uses the ODBO driver to connect to SAP HANA. It’s the only BI tool that uses

this driver. The ODBO driver implies that it uses MDX, which means it can read

cubes, and Excel is able to consume SAP HANA calculation cube views. You can

actually see this in Figure 3.19 in the dialog box on the left.

SAP HANA Providing the Semantic Layer

If you used older reporting or BI tools, you’ll know that you had to add a semantic

layer between the data sources and your reports. Pure data, for example, doesn’t

have hierarchies defined, but you need hierarchies when users want to drill down

deeper into the data. SAP BusinessObjects Universe Designer was used for creating

“universes,” and this evolved into the Information Design Tool while the “uni-

verses” evolved into semantic layers. These tools combined multiple data sources

into harmonized models that could be used for reporting.


You’ll see in the following chapters how SAP HANA provides these features while

you’re creating your information models. SAP also uses this in its various prod-

ucts, such as SAP S/4HANA and SAP C/4HANA. The raw data is combined and fil-

tered, additional fields are calculated, aggregates are created, currencies are

converted, and semantics are added in the models. When you expose the models,

they are ready for consumption by any reporting tool—even a 30-year-old tool

such as Excel!

You’ve now seen how SAP HANA architecture works, from the hardware with

memory and CPU cores, all the way up to the cloud platforms and analytics.

Important Terminology

The following important terminology was covered in this chapter:

� In-memory technology

In-memory technology is an approach to querying data residing in RAM, as

opposed to physical disks. The results of doing so lead to shortened response

times. This enables enhanced analytics for BI/analytic applications.

� SAP HANA architecture

No longer constrained by disks, other solutions were added, including column-

based storage, compression, no indices, delta merges, scale-out, partitioning,

and parallelism.

With the paradigm shift to in-memory technologies, various programming lan-

guages and application development processes were added to SAP HANA.

SAP HANA still uses disks but purely as secondary storage (backup) because

memory is volatile. With the data and log volumes, SAP HANA ensures that no

data ever gets lost. The data and log volumes are also used for backups, when

starting up the SAP HANA system, and for disaster recovery.

� Persistence layer

The disk storage and data storage happens in the persistence layer, and storing

data on disk is called persistence.

� SAP HANA as a side-by-side accelerator

Source systems stay as they are, and SAP HANA is added on the side to acceler-

ate any transactions or reports that have performance issues in the source sys-

tems. The relevant data is duplicated from the source system to SAP HANA.

This duplication is normally performed by the SAP Landscape Transformation

Replication Server. The copied data in SAP HANA is then used for analysis or

Important Terminology Chapter 3 115

reporting. SAP provides prebuilt models in SAP HANA to accelerate processes

such as profitability data. SAP Landscape Transformation Replication Server

replicates just the required profitability data from the SAP ERP system to the

SAP HANA system.

� SAP HANA as a database

SAP HANA is used to replace the databases under SAP BW, SAP CRM, and SAP

ERP (all part of the SAP Business Suite). There are well-established database

migration procedures to replace the current database with SAP HANA. In each

case, SAP HANA replaces the database only.

� SAP HANA as a platform

SAP HANA is a development platform. By combining the SAP HANA informa-

tion models with development in the SAP HANA XS application server, we can

build powerful custom web applications.

� Public cloud

A public cloud is available to anyone, offering any type of cloud service. The

infrastructure is shared by members of the public. You don’t get your own ded-

icated machine at Amazon or any other public cloud provider.

� Private cloud

A private cloud offers the same features that you would find in a public cloud,

but it’s dictated by different forms of access. With a private cloud, the servers in

the cloud provider’s data center are dedicated for your company only. No other

companies’ programs or data are allowed on these servers.

� Hybrid cloud

A hybrid cloud involves either combining a public and private cloud offering or

running a private and public cloud together. With a hybrid cloud, you connect

your private cloud’s server back to your own data center via a secure network

connection. In that case, the servers in the cloud act as if they are part of your

data center, even though they are actually in the cloud provider’s data center.

� SAP HANA Enterprise Cloud

SAP HANA Enterprise Cloud offers all the SAP applications as cloud offerings.

You can choose to run SAP HANA, SAP BW, SAP ERP, SAP CRM, SAP Business

Suite, and many others as a full-service offering in the cloud. All the systems

that you would normally run on premise are available in SAP HANA Enterprise

Cloud.

� SAP Cloud Platform

This PaaS allows you to build, extend, and run applications in the cloud. This

includes SAPUI5, HTML5, SAP HANA XS, Java applications, and more.


� SAP HANA extended application services, classic model (SAP HANA XS)

SAP HANA XS embeds an application server into SAP HANA and allows develop-

ers and modelers to use SAP HANA as a platform. It uses the MVC paradigm for

creating web services.

� SAP HANA extended application services, advanced model (SAP HANA XSA)

SAP HANA XSA is the new expanded version of SAP HANA XS that is used exten-

sively in SAP HANA 2.0.

Practice Questions

These practice questions will help you evaluate your understanding of the topics

covered in this chapter. The questions shown are similar in nature to those found

on the certification examination. Although none of these questions will be found

on the exam itself, they allow you to review your knowledge of the subject. Select

the correct answers, and then check the completeness of your answers in the

“Practice Question Answers and Explanations” section. Remember, on the exam,

you must select all correct answers and only correct answers to receive credit for

the question.

1. Which technologies does SAP HANA use to load more data into memory?

(There are 3 correct answers.)

� A. Eliminate indices.

� B. Use ZIP compression.

� C. Use dictionary compression.

� D. Store data in column tables.

� E. Use multicore CPU parallelism.

2. True or False: With SAP HANA, you can run OLAP and OLTP together in one

system with good performance.

� A. True

� B. False

Practice Questions Chapter 3 117

3. True or False: SAP HANA contains both row and column tables.

� A. True

� B. False

4. How do you move code to SAP HANA? (There are 2 correct answers.)

� A. Delete the application server.

� B. Use stored procedures in SAP HANA.

� C. Use graphical data models in SAP HANA.

� D. Use row tables in SAP HANA.

5. In which SAP HANA solution do you eliminate stored data, such as year-to-

date figures?

� A. SAP BW on SAP HANA

� B. SAP CRM on SAP HANA

� C. SAP S/4HANA

� D. SAP HANA Enterprise Cloud

6. Which deployment scenario can you use to accelerate reporting from an old

SAP R/3 system?

� A. SAP HANA as a database

� B. SAP HANA as a platform

� C. SAP HANA as a side-by-side accelerator

� D. SAP HANA multitenant database containers (MDC)

7. Which deployment scenarios feature security staying in the application

server and end users not logging in to the SAP HANA system? (There are 2 cor-

rect answers.)

� A. SAP HANA as a platform with an SAP HANA XS application

� B. SAP HANA as a database

� C. SAP HANA as a side-by-side accelerator

� D. SAP HANA as a reporting server


8. For which deployment scenario does SAP deliver prebuilt data models?

� A. SAP HANA as a platform

� B. SAP HANA as a side-by-side accelerator

� C. SAP HANA as an SQL Data Warehouse

� D. SAP HANA on the cloud

9. True or False: SAP HANA is just a database.

� A. True

� B. False

10. What is normally used to build SAP HANA XS applications inside SAP HANA?


� A. Java

� B. JavaScript

� C. SAPUI5

� D. ABAP

11. Which type of cloud solution is SAP HANA Enterprise Cloud an example of?

� A. Platform as a service (PaaS)

� B. Infrastructure as a service (IaaS)

� C. Software as a service (SaaS)

� D. Managed cloud as a service (MCaaS)

12. Which type of cloud solution is Amazon Web Services (AWS) an example of?

� A. Hybrid cloud

� B. Public cloud

� C. Private cloud

� D. Community cloud


13. True or False: VMware vSphere virtual machines are fully supported for pro-

ductive SAP HANA instances.

� A. True

� B. False

14. You use parallelism to calculate a year-to-date value quickly, which eliminates

a table from the database. What are some of the implications of this action?


� A. Smaller database backups

� B. Less code

� C. Better user interfaces

� D. Faster response times

� E. Faster inserts into the database

15. What enables the delta buffer technique to speed up both reads and inserts in

SAP HANA?

� A. Using column-based tables only

� B. Using row-based tables only

� C. Using both row-based and column-based tables

� D. Using only the insert-only technique

16. What technique is used to convert record updates into insert statements in

the delta buffer?

� A. Unsorted inserts

� B. Insert-only

� C. Sorted inserts

� D. Parallelism

17. What phrase describes the main table area in the delta merge scenario?

� A. Read-optimized

� B. Write-optimized

� C. Unsorted inserts

� D. Row store


18. What does the C in ACID-compliant stand for?

� A. Complexity

� B. Consistency

� C. Complete

� D. Constant

19. Which of the following are results of deploying SAP HANA as a distributed

(scale-out) solution? (There are 2 correct answers.)

� A. Disaster recovery

� B. High availability

� C. Failover

� D. Backups

20. Which words are associated with the log volume? (There are 2 correct

answers.)

� A. Synchronous

� B. Transaction manager

� C. Page manager

� D. Savepoint

21. Of which SAP HANA process are the transaction manager and page manager a

part?

� A. Application server

� B. Statistics server

� C. Index server

� D. Name server

22. What type of load does SAP HANA perform when starting up?

� A. Lazy load

� B. Complete load

� C. Fast load

� D. Log load


23. What does SAP HANA load when starting up and before indicating that it’s

ready? (There are 3 correct answers.)

� A. All row tables

� B. All system tables

� C. All partitions

� D. Some column tables

� E. Some row tables

24. Which type of tables required extended storage?

� A. Row tables

� B. Multistore tables

� C. Column tables

� D. In-memory tables

25. What do you need for active/active (read-enabled) mode? (There are 2 correct

answers.)

� A. Log replication

� B. Extended storage

� C. Disaster recovery

� D. Multitenant database containers (MDC)

26. What SAP HANA feature do you use to host multiple applications for different

companies in a single SAP HANA landscape while ensuring isolation?

� A. SAP HANA as a distributed database

� B. Extended storage

� C. Active/active (read-enabled) mode

� D. Multitenant database containers (MDC)

27. Which component of the SAP SQL Data Warehousing do you use to move

tables with data that are used together often to the same node to enhance

query performance?

� A. Data Distribution Optimizer (DDO)

� B. Data Lifecycle Manager (DLM)


� C. Data Warehouse Monitor (DWM)

� D. Data Warehouse Scheduler (DWS)

28. Which BI tool queries SAP HANA using MDX?

� A. SAP Analysis for Microsoft Office

� B. SAP Lumira, discovery edition

� C. SAP BusinessObjects Web Intelligence

� D. Microsoft Excel

29. You want to publish your BI content directly to SAP Analytics Cloud. Which

tool do you use?

� A. SAP Lumira, discovery edition

� B. SAP Crystal Reports

� C. SAP Analysis for Microsoft Office

30. Which of the following ways of publishing SAP HANA information model con-

tent could require some JavaScript knowledge?

� A. Creating BI content with SAP Lumira, designer edition

� B. Creating a CompositeProvider in SAP BW

� C. Creating a report in SAP Crystal Reports

Practice Question Answers and Explanations

1. Correct answers: A, C, D

SAP HANA uses column tables, dictionary compression, and eliminating indi-

ces to load more data into memory.

However, it doesn’t use ZIP compression because ZIP-compressed files would

have to be uncompressed before the data could be used.

Multicore CPU parallelism helps with the performance but doesn’t directly

address the data volume issue. It contributes indirectly because, by using it, we

can eliminate things such as year-to-date tables as in SAP S/4HANA.

Practice Question Answers and Explanations Chapter 3 123

2. Correct answer: A

Yes, with SAP HANA, we can run OLAP and OLTP together in one system with

good performance. Normally, this isn’t possible in the traditional database sys-

tem.

Note

There are no True-False questions in the actual certification exam, but we use them here

to help you check if you understood this chapter.


Yes, SAP HANA contains both row and column tables.

4. Correct answers: B, C

Use stored procedures and graphical data models to execute the code in SAP

HANA.

Deleting the application server won’t help with running the code in SAP HANA,

unless we develop all that same functionality in the SAP HANA XS server.

Using row tables in SAP HANA has nothing to do with executing code.

5. Correct answer: C

In SAP S/4HANA, many aggregate tables are removed, and the solution is sim-

plified.


SAP HANA as a side-by-side accelerator doesn’t change the source systems.

7. Correct answers: B, D

With SAP HANA as a side-by-side accelerator and platform, users have to log in

to SAP HANA.

With SAP HANA as a database and reporting server, users log in via the applica-

tion server or reporting tools.

8. Correct answer: B

SAP delivers prebuilt data models for the special cases of the side-by-side accel-

erators, for example, for profitability analysis.


False. SAP HANA isn’t just a database. SAP HANA can also be used as a platform.

Databases don’t normally have application servers, version control, code repos-

itories, and various programming languages built in.



SAP HANA XS applications in SAP HANA are built using JavaScript and SAPUI5.

Java and ABAP aren’t part of SAP HANA XS.

With SAP HANA XSA, it’s possible to run other programming languages, such

as Java and C++.

11. Correct answer: D

SAP HANA Enterprise Cloud is a managed cloud as a service (MCaaS).


Amazon Web Services (AWS) is a public cloud.


True. Productive SAP HANA is supported on VMware vSphere.

14. Correct answers: A, B, D

The implications of eliminating such tables from the database are smaller data-

base backups, less code, and faster response times.

Doing so won’t necessarily improve user interfaces.

It actually eliminates extra inserts into the database but doesn’t make the cur-

rent data inserts any faster. Techniques such as removing indices will achieve

that.


The delta buffer technique speeds up both reads and inserts in SAP HANA

because it uses both row-based and column-based tables.

Using column-based tables only speeds up reads, not inserts. Using row-based

tables only speeds up inserts, not reads.

Using only the insert-only technique is useless without having both row-based

and column-based tables.

Tip

Watch out for the word “only” in a question or in an answer option.


The insert-only technique is used to convert record updates into insert state-

ments in the delta buffer.

Unsorted inserts are used in the delta buffer, but they don’t convert updates to

insert statements.

Sorted inserts aren’t used in the delta buffer.


Parallelism isn’t relevant in this case.


The main table area in the delta merge scenario is read-optimized.

The delta store is write-optimized, uses unsorted inserts, is uncompressed, and

is based on the row store.


ACID stands for atomicity, consistency, isolation, and durability.


Deploying SAP HANA as a distributed (scale-out) solution results in high avail-

ability and failover capability.

Disaster recovery and backups work whether the SAP HANA system is distrib-

uted or on a single server.

20. Correct answers: A, B

Synchronous and transaction manager are associated with the log volume.

Page manager and savepoint are associated with the data volume.


The transaction manager and page manager are part of the index server.

The statistics server and name server services are also part of SAP HANA, but

they aren’t discussed in this book.


SAP HANA performs a lazy load when starting up.


During the lazy load, SAP HANA loads all row tables, all system tables, and some

column tables.

SAP HANA doesn’t have to load all partitions of a table.


Multistore tables require extended storage. The other tables will work whether

we use extended storage or not.

25. Correct answers: A, C

You need log replication and disaster recovery for active/active (read-enabled)

mode.

Extended storage separates data between a memory (hot) store and a disk

(warm) store.

Multitenant database containers (MDC) separate tenants.



You use multitenant database containers (MDC) to host multiple applications

for different companies in a single SAP HANA landscape while ensuring isola-

tion.

SAP HANA as a distributed database is a single SAP HANA system spread across

multiple machines, but it’s normally a single application and doesn’t ensure

isolation for multiple companies from each other.

Extended storage spreads the data of a single SAP HANA system across a hot

and a warm tier. It’s normally a single application and doesn’t ensure isolation

for multiple companies from each other.

Active/active (read-enabled) mode allows users to read data for tasks such as

reporting from a replica copy of the SAP HANA system. It’s normally a single

application and doesn’t ensure isolation for multiple companies from each

other.


You use the Data Distribution Optimizer (DDO) component of SAP SQL Data

Warehousing to move tables with data that are used together often to the same

node to enhance query performance.


Microsoft Excel queries SAP HANA using MDX via the ODBO driver. It’s the

only BI tool discussed that uses the ODBO driver.


SAP Lumira, discovery edition, can publish your BI content directly to SAP Ana-

lytics Cloud.


JavaScript knowledge may be required for SAP Lumira, designer edition.

Takeaway

You now understand in-memory technologies such as column-based storage,

compression, no indices, delta merges, scale-out, partitioning, and parallelism.

You also know how SAP HANA uses disks for its persistence storage.

You can now describe the various SAP HANA deployment scenarios; how SAP

HANA uses in-memory technologies such as columns, tables, and compression;

and the various ways that SAP HANA can be deployed. We looked at the different


advantages of each deployment method to help you decide which one fits your

requirements best. Finally, we looked at how SAP HANA can also be deployed in

the cloud and for multiple tenants.

Summary

You’ve learned the various ways you can deploy SAP HANA. With this knowledge,

you can make recommendations to business users for an appropriate system

architecture and landscape.

In the next chapter, we’ll discuss changes that SAP HANA XSA brought to the SAP

HANA architecture and modeling processes. We’ll also look at the tools with which

we create information models and how to manage and administrate these models.

Chapter 4

Modeling Tools, Manage-ment, and Administration


� Understand the new modeling and development process in

SAP HANA 2.0

� Learn how the new SAP HANA XSA architecture complements

SAP Cloud Platform

� Explain the difference between design-time and runtime objects

� Identify the different working areas in SAP Web IDE for

SAP HANA

� Get to know the SAP HANA modeling tools

� Learn how to complete the different steps in a project’s model-

ing and development process

� Learn how to work with tables and data

� Know how to create new information models

� Understand how to solve build errors by learning about the

SAP HANA XSA build processes

� Understand the deployment process and learn how it fits into

SAP’s transport processes

� Describe the administration and management steps required in

a project

� Perform refactoring techniques on information models

Chapter 4 Modeling Tools, Management, and Administration130

Tip

If you haven’t worked with SAP HANA 2.0 before, this will possibly be the most important

chapter for you.

You now understand where SAP HANA came from and how it works from an archi-

tectural point of view. SAP HANA has been growing fast, but the world hasn’t stood

still. There has been progress in many different areas, such as cloud platforms,

containers, development best practices, new programming paradigms, microser-

vices, NoSQL databases, machine learning, blockchain, Internet of Things (IoT),

DevOps, continuous integration, and, perhaps most of all, customer expectations.

SAP HANA 2.0 is, in some sense, a response to this progress. In this chapter, we’ll

look at what is new in SAP HANA 2.0 from many different angles. First, we’ll look at

the new modeling and development process. This information is necessary to put

many of the new changes into context. These changes include the new architec-

ture, the new web-based user interface (UI), and the new tools. We’ll also cover how

to start a modeling project, how to create and run information models, and how to

get them into the production system within the new system. You’ll understand

why the system behaves the way it does; when you get errors, you’ll know why and

how to solve them.

We’ll start with some process and architecture diagrams before moving on to the

actual system itself. We’ll describe the steps you need to get going with SAP HANA

modeling on your own system and your own projects.

Real-World Scenario

You’re working on a project that requires you to create information models.

To do so, you use SAP Web IDE for SAP HANA to create and edit these views

graphically. You can use this tool from anywhere, even when using cloud sys-

tems.

Sometimes you reach for the other tools in your toolkit: SAP HANA database

explorer, SAP HANA cockpit, SAP HANA XSA cockpit, and SAP Enterprise

Architecture Designer (SAP EA Designer).

When working on a project with SAP HANA, you’ll use modeling tools all the

time. You have to be familiar with the SAP HANA modeling tools, know when

to use which one, and be able to find your way around the various screens.

Objectives of This Portion of the Test Chapter 4 131

SAP HANA has grown over the past few years, and the modeling tools now

help you accomplish a wide variety of tasks. The features have also grown to

provide rich functionality. It therefore takes some time to learn how to find

your way around the various menus, options, and screens.

All the information models you created for your project are in your develop-

ment system. You feel confident about the work you’ve created, so now it’s

time to take it to the production system. You need to transport your infor-

mation models through the SAP HANA landscape. Before end users can use

your information models in the production system, your models need to be

deployed to the production system.

As the project progresses, new customer requirements arise. Your informa-

tion models need to adapt to changing business conditions. You ensure they

do so by using the refactoring tools built in to the SAP HANA modeling tools.


The objective of this portion of the SAP HANA certification is to test your under-

standing of how to create, manage, build, deploy, and refactor your information

models with the various SAP HANA modeling and development tools. Your insight

will be tested by describing various scenarios that result in error messages, and

you have to know why the errors were triggered.

The certification exam expects you to have a good understanding of the following

topics:

� The modeling and development process in SAP HANA 2.0

� The SAP HANA extended application services, advanced model (SAP HANA XSA)

and SAP HANA Deployment Infrastructure (HDI) architecture

� Differences between design-time and runtime objects

� SAP Web IDE for SAP HANA and the SAP HANA modeling tools

� The different steps in the complete modeling and development process of a

project, from creating a project, to working with tables and information models

� Solving build errors by understanding the SAP HANA XSA build processes

� The deployment process and how this fits in with SAP’s transport processes

� The administration and management steps required in a project

� The various refactoring tools and techniques available


Note

This portion contributes up to 10% of the total certification exam score.


In the previous chapter, we briefly introduced you to SAP HANA extended applica-

tion services, classic model (SAP HANA XS). We’ll now begin by describing SAP

HANA XS in a bit more detail, and then move on to SAP HANA extended applica-

tion services, advanced model (SAP HANA XSA) with HDI.

Note

In the What’s New Guide for SAP HANA 2.0 SPS 03, in the section for SAP HANA XS and

the SAP HANA repository, it says,

“SAP HANA Extended Application Services classic model (XS classic) and SAP HANA Reposi-

tory are deprecated as of SAP HANA 2.0 SPS 02.”

You can also see SAP Note 2465027 for more details.

New Modeling Process

In SAP HANA 1.0, we had two main working areas, as shown in Figure 4.1. We had

the Content area, where we created our information models. These models were

stored in a repository inside SAP HANA. In addition to our models, we could also

store other files there, such as HTML and JavaScript code. The files in the SAP

HANA repository are known as design-time files because we edit them to design

our models and code.

When we activated these models, a copy got stored in the _SYS_BIC schema in the

Catalog area, and the _SYS_REPO user became the owner of these files. These copies

are available to users, which they then use to run analysis, reports, and applica-

tions on the system. These copies were known as the runtime files because the

users used them to run their queries. These files were stored together with the

database tables and the data. Tables were grouped together in schemas.

By storing and working with the design-time and runtime files in two different

areas, there is a separation between design-time and runtime files. We used trans-

ports to copy the design-time files to a production environment, where we could


easily create a new set of runtime files. We could transport single information

models or bundles of models together in packages.

Figure 4.1 SAP HANA XS

SAP HANA used SAP HANA XS as an application server for serving web services to

applications. SAP HANA XS was also known as a runtime system, as it was running

a web server and the web-based application code. It was easy to use because it was

directly built into the SAP HANA system.

Originally, SAP HANA Studio was the only modeling and development environ-

ment, but later SAP HANA Web-Based Development Workbench also appeared.

As time went on, a number of issues cropped up with SAP HANA 1.0, as follows:

� Because SAP HANA XS is built into SAP HANA, it runs on the same expensive

hardware that SAP HANA uses.

SAP HANA Studio (modeler, developer)• Modeler perspective• Developer perspective• Administration Browser (end-user)

BI tools (end-user)

SAP HANA

ContentSAP HANA XSC run-time

View (HTML)

Static content

SAPUI5

User Auth

Controller

xsjs

OData (JavaScript)

Catalog (SAP HANA Database)

Classic databaseschema

Repository for design-time objects• CalcViews• SQLScript• CDS• Roles• HTML• xsjs• Etc.

Classic databaseschema

Activate

_SYS_BIC schema(run-time objects)• Col. Views


� There was only one copy of the SAP HANA XS application server for an SAP

HANA system, leading to scalability limitations.

� The tight coupling of SAP HANA XS in SAP HANA servers led to some problem-

atic networking designs.

� XS JavaScript and the associated development process were quite different from

the latest advances in the JavaScript development community. When compa-

nies need skilled people, it helps if the development processes they use fit in

with what the rest of the industry does. New people can get up to speed quicker,

and support improves in case of problems.

� The built-in repository lacked certain vital features like version control, forking,

and merging branches.

� Table definitions weren’t transportable. Core data services (CDS) was intro-

duced in the later versions of SAP HANA 1.0 to address this.

SAP also needed to address some industry requirements:

� SAP started using SAP HANA in more of its cloud business systems. Due to fea-

ture differences between the cloud and the on-premise offerings, better feature

parity was needed between these offerings.

Tip

Many of the changes in SAP HANA 2.0 and SAP HANA XSA can be summarized by three

words: cloud, containers, and microservices.

� The development and modeling processes in the cloud environments were differ-

ent from that of the on-premise solutions. Web-based UIs are vital for cloud sys-

tems. Best practices such as the twelve-factor app, found at https://12factor.net/,

for cloud-native development emerged and got adopted. People wanted a consis-

tent way to work with both cloud and on-premise development and modeling.

� The speed of updates in cloud offerings is much faster than that of their on-

premise cousins, requiring new ways of providing application services. System

administrators and developers now use new automated build and testing pro-

cesses, DevOps, continuous integration, continuous delivery, blue-green

deployment, and so on.

� Cloud environments were enabled by the arrival of virtual machines. Over time,

people started adding more and more virtual machines to servers, and certain

limits showed up. This led to the concept of containers, which are much smaller,

can start very quickly, and can scale easily when used with cloud platforms. The

combination of containers and web services led to the emergence of microser-

https://12factor.net/


vices architectures. There was a need for a way to make use of these new con-

cepts.

� A microservices architecture needs a runtime to manage the various containers

and the microservices running in each container. SAP decided to use Cloud

Foundry for this. Cloud Foundry is an open-source, multi-cloud application

platform as a service (PaaS) offering. The new SAP Cloud Platform uses Cloud

Foundry.

� Cloud hyperscalers, such as Amazon, Microsoft, and Google, are used by many

SAP customers. The Cloud Foundry PaaS, containers, and microservices devel-

oped with SAP run on the offerings from these companies. SAP customers can

this leverage their other development and solutions they already have running

on these cloud infrastructures.

These gaps in coverage led to the development of SAP HANA XSA, which brought a

new modeling and development process with it, as shown in Figure 4.2. In the fig-

ure, the process moves from the left to the right. One of the first things to notice is

that there is now a total separation of the design-time environment (left side)

from the runtime environment (right side). Where these were previously together

in an SAP HANA system, they are now completely separate.

Note

You might have noticed already that we keep mentioning modeling and development in

the same breath. You’ll use development objects in your information models, and your

information models will be used by developers in their web applications. SAP HANA mod-

eling has become an integral part of the development process.

The process starts in the top-left corner of Figure 4.2. You use SAP Web IDE for SAP

HANA to start a project, create and edit information models, and then build them.

Tip

If you worked a lot with SAP HANA 1.0, you might be tempted to continue using SAP

HANA Studio. You can’t use SAP HANA Studio to develop SAP HANA XSA applications

because it doesn’t work with the new SAP HANA HDI infrastructure. You need to use SAP

Web IDE for SAP HANA.

Your models get stored in a Git repository instead of an SAP HANA repository. Git

is the most-used repository by developers worldwide. This allows for version

control, forking, and merging. Git integration is built into the SAP HANA XSA mod-

eling and development tools.


Figure 4.2 The New SAP HANA Development and Operations Process

The build process in SAP HANA 2.0 is similar to the activation process in SAP

HANA 1.0. It creates the runtime versions of the objects in the development sys-

tem. In this case, the development system is referred to as a space. We’ll explain

this in more detail in later sections. You run your models in the development

space to test them.

Note

When you install SAP HANA, express edition, you’ll get all this on your laptop or cloud

instance.

When you’re happy with your models and code, you can synchronize them with

the enterprise-wide Git server. This is one of the big changes in SAP HANA 2.0.

Instead of having a repository in each SAP HANA system with multiple copies of

the same design-time file in each system in the landscape, all the design-time files

Administrator/OperationsModeler, Developer

Modeler, Dev, Team Lead

SAP HANA XSA Jenkins(automation tool)

Landscape/Staging

SAP HANA XSA

SAP HANA XSA

SAP HANA XSA

Production Spaces(Blue/Green)

QA/Test Space

CI Space

Release Job

Deploy Job

Build Job

Development Space(local runtime)

SAP Web IDE for SAPHANA, code editor

Gerrit

Git(local)

Code review,Approvals

Create,Edit

Run, Test,Debug

Build

Trigger the Build

Design-time Run-time

Git(Enterprise-wide)

End-users


are now in one master repository. Git allows for many different version and

branches. You have a stable production branch, but you can also have different

development branches where people are trying out different new ideas. When the

idea gets accepted, the code is merged into the integration and testing branches.

You can fork a branch, which creates a copy, and test out something new in a sand-

box. Git allows large teams of developers to seamlessly work together on large or

complex software projects.

You can also use Gerrit, which is Git with some additional features added, such as

enabling code reviews and approvals. The team lead can sit with modelers and

developers to review their work and approve the changes.

Tip

In this book, we’ll focus on the process up to this point. In this section, we give a descrip-

tion of the rest of the process to give you an understanding of how your work gets into

the production system where end users can start using it and the business can start get-

ting value out of it.

In many cases, the update in the enterprise-wide Git or the approval in Gerrit, trig-

gers an automated build of the project, and a build job updates the changes into

the continuous integration (CI) space (system) where you can see how your mod-

els and code work together with everybody else’s.

The build job is set up, configured, and managed by the administrators as part of

the operations area. Many times, they need to understand what your code is trying

to do and what it will change. On the other hand, you need to know how, when, and

where your code gets used. This collaboration has led to DevOps; development

and operations working together.

The administrators (operations) can use something such as Jenkins, which is an

open-source automation server that is used to automate tasks related to the build-

ing, testing, and deploying of software.

Quality assurance (QA) testers can trigger a deploy job. Testing users can then test

everyone’s updates in an environment that is very similar to the production envi-

ronment. This is also sometimes called the staging server.

After all testing has been approved, a release job is triggered that updates the pro-

duction system. To ensure continuous uptime on the production servers, a blue-

green deployment strategy can be implemented. You can read more about this at

http://s-prs.co/v487624 and http://s-prs.co/v487625.




To summarize, Table 4.1 lists the differences between SAP HANA 1.0/SAP HANA XS

and SAP HANA 2.0/SAP HANA XSA discussed in this section.

We’ll now look at the SAP HANA XSA architecture to see how all the remaining

concerns mentioned in the previous section were addressed. After that, we’ll get

into the SAP HANA system and learn how to practically implement the process we

discussed here.

New Architecture for Modeling and Development

We mentioned earlier that many of the changes in SAP HANA XSA can be summa-

rized by cloud, containers, and microservices. This comes out quite clearly in

Figure 4.3, which shows the similarities and compatibility between SAP HANA XSA

and SAP Cloud Platform architectures.

SAP HANA 1.0 and SAP HANA XS SAP HANA 2.0 and SAP HANA XSA

Embedded SAP HANA repository for each

SAP HANA system

Enterprise-wide Git server, external to

SAP HANA

SAP HANA Studio and SAP HANA

Web-Based Development Workbench

SAP Web IDE for SAP HANA

Activate process Build process

Development server Development space

Transports Deploys

SAP-specific development and transport

processes

Industry-accepted processes such as

DevOps, CI, and blue-green deployments

Proprietary SAP tools Open-source tools and platforms such as

Git, Gerrit, Jenkins, and Cloud Foundry

Design-time and runtime objects together

in an SAP HANA system

Design-time objects stored in Git

Only runtime objects in an SAP HANA sys-

tem

Table 4.1 Changes in the Modeling Process from SAP HANA 1.0 to SAP HANA 2.0


Figure 4.3 Similarities and Compatibility between SAP HANA XSA and SAP Cloud Platform Architectures

Both architectures have three layers. The bottom layer is different between SAP

HANA XSA and SAP Cloud Platform. SAP HANA XSA is running on servers, which

can be on premise or on the cloud. SAP Cloud Platform is running on OpenStack,

an open-source infrastructure as a service (IaaS) platform, in the SAP data centers

or on the cloud platforms of Amazon, Google, and Microsoft.

The next layer of both, the platforms, are getting a lot closer. Both SAP HANA XSA

and SAP Cloud Platform are based on Cloud Foundry. Both add additional services

to enhance the standard Cloud Foundry offering, such as routing, security, sched-

uling, integration, UI framework (SAPUI5), database services (e.g., the SAP HANA

database), and so on. These are shown as the Backing Services in the diagram.

The top layer is really where it starts making sense for modelers and developers.

The application and HDI containers are identical on both SAP HANA XSA and SAP

Cloud Platform.

Containers and virtual machines are both used to isolate applications and their

dependencies into a single “unit” that can run anywhere. Both remove the need

for physical hardware. This enables efficient use of servers in terms of energy

SAP HANA XSA

Servers

HDI Containers

Backing Services

UAA, Job scheduler, Connectivity, UX, Data Quality, APIs, Integration, Mobile, Machine Learning, IoT, …

SAP HANA database platform

SAP HANA XSA runtime

Application Containers

SAP Cloud Platform with Cloud Foundry

Additional Platform Services

IaaS

OpenStack, Private Cloud, Multi-Cloud (Amazon WS, MS Azure, Google CP)

SAP Cloud Platform

Cloud Foundry

HDI Containers

Application Containers

Backing Services

UAA, Job scheduler, Connectivity, UX, Data Quality, APIs, Integration, Mobile, Machine Learning, IoT, …Redis, PostgreSQL, RabbitMQ, MongoDB

Containers and services

Platform layer

Infrastructure layer


costs. However, where virtual machines require an operating system for each vir-

tual machine, containers use new features in modern operating systems that

allow them to create thousands of small isolated units to run on a single server and

operating system.

Containers fully isolate the application, the users, the memory and CPU usage, and

the network. Their popularity in recent years stems from the fact that they scale

fast and easily. Say you have a small web service running in a container. When

there aren’t many requests for this service, only one container is sufficient. As the

load increases, the platform layer—also known as the container runtime—auto-

matically creates more copies of the container to meet the increased demand.

When the load decreases, the number of copies is also reduced, which is a huge

benefit for companies that can’t predict how much hardware is required for a ser-

vice or application.

The container isolation helps teams work at full speed on their own code. One

team can work, for example, on the financial calculations service, while another

team focuses on the UI. Both teams can deploy updates to the production system

independently from each other.

Tip

The independence of containers, referred to as loose coupling, is important for continu-

ous integration (CI) and continuous deployment (CD).

Another advantage is that you can now use the programming language that best

suits your needs for that particular service. You could use JavaScript for the UI and

Golang for the financial calculations service. This approach is sometimes called

“bring your own language” (BYOL).

Even though you can use multiple programming languages, the container run-

time makes this appear seamless to end users via the routing service. The UI ser-

vice could use a URL such as https://server/ui, whereas the financial calculations

service could use https://server/fincalcs. The routing service directs (routes) the

user requests to the correct services in the different containers. If you use a stan-

dard UI framework, such as SAPUI5, all your applications will have a consistent

look and feel, despite the fact that the underlying services are using different pro-

gramming languages.

Each container also contains the environment required to run the services contained

in it. A Java service might require Tomcat, whereas PHP (Hypertext Preprocessor)

https://server/ui

https://server/fincalcs


might require the Apache web server running inside its container. This architecture is

also known as microservices.

Note

The microservices architecture allows us to develop services and applications on SAP

HANA XSA (including the express edition) in an on-premise environment and then deploy

it to SAP Cloud Platform!

What we’ve just described is all part of the new SAP HANA architecture. You can

see the various aspects, such as routing, UI services, containers, BYOL, and the XSA

container runtime, in Figure 4.4.

Figure 4.4 SAP HANA XSA Architecture

The three blocks on the left are external to SAP HANA. We’ve already seen that Git

is used as an enterprise-wide repository. Security for end users moves from within

SAP HANA to using an external Identity Provider (IdP) system. Business users and

technical users are separated. We discuss this in more detail in Chapter 13.

Browser (modeler, developer)• Web IDE for SAP HANA• Database Explorer• Etc. Browser (end-user)

BI tools (end-user)

SAP HANA

Multi Target Application (MTA)

Application Container

Bring Your OwnLanguage (BYOL)/

Runtime

HTML5 App,static content Tomcat/TomEE

OData (Java)

Java App

User Accountand Auth(UAA)

Node.js

Node App

OData (JavaScript)

SAP HANA extended application services, advanced model (XSA) runtime/Cloud Foundry

Application Router

SAP HANA Database

IdentityProvider (IdP)

SAP HANA Deployment Infrastructure (HDI)

HDI ContainerHDI Container

Backing servicesRepository (Git)for design-timeobjects• Calc Views• SQLScript• CDS• Synonyms• Roles• HTML• Node.js• Java• Etc.


The SAP HANA system has two major areas: (1) the SAP HANA database at the bot-

tom of the diagram, and (2) SAP HANA XSA. You can run SAP HANA XSA inside the

SAP HANA system on the same server as the SAP HANA database (e.g., SAP HANA

XS), but you can also separate it to run on one or more other cheaper servers. This

addresses the first two requests in the list, as these cheaper servers can scale

cheaply and easily, and the network configurations can be hardened. This is indi-

cated by the dashed line between the two areas.

In SAP HANA XSA, you can combine services in different programming languages

into a one application by creating a multi-target application (MTA) project. You

can write one service in Java, another in JavaScript, and yet another in a language

of your choice. During deployment, SAP HANA XSA will automatically create dif-

ferent application containers for each of these services. This multi-language con-

figuration is specified by the mta.yaml file in your project. You can use either the

SAP HANA XSA runtime or SAP Cloud Platform to “host” these application con-

tainers.

Tip

SAP HANA XSA and SAP Cloud Platform use the industry-accepted Node.js when working

with JavaScript.

In the SAP HANA database area, you’ll notice something interesting. Each schema

in an SAP HANA XSA database is now a container.

In our project, you add an SAP HANA database (HDB) module. In this module

(folder), you add all your design-time objects, such as tables, calculation views, and

SQL procedures. The HDB module definition is also added to the mta.yaml file. The

HDB module is for your design-time objects.

When you build your HDB module, it creates an SAP HANA database container.

This replaces the classical database schemas in the older version of SAP HANA.

These containers that contain just SAP HANA database runtime objects are called

HDI containers. They contain only runtime objects, as all design-time objects are

stored in Git, even the design-time database objects. The HDI container function-

ality is part of the SAP HANA runtime and is part of SAP Cloud Platform runtime

should you choose to deploy your HDI containers on the cloud.

One of the first problems you might encounter is due to the fact that containers

provide full isolation. This radically impacts all SAP HANA security and can be frus-

trating when you’re used to the old way of working.


Tip

In this book, and in the exam, the focus is exclusively on the HDB modules in the design-

time environment that become HDI containers in the runtime environment. The exam is,

after all, about SAP HANA modeling, which gets designed in HDB modules and executed

in HDI containers.

SAP HANA XSA development with containers and microservices is addressed with the SAP

HANA development exam. These two areas complement each other, and there are over-

laps. The SAP HANA XSA courses on openSAP focus more on this area, with the result that

many developers neglect SAP HANA modeling because they aren’t aware that it could, in

many cases, make their lives a lot easier.

Let’s look at an example, as shown in Figure 4.5. The top half shows an example of

a calculation view (CV2) that uses tables from two other schemas. In SAP HANA 1.0,

we would just drag and drop table T1 from the one schema and table T2 from the

other schema, and join them in a view in a third schema. All this happened quickly

and effortlessly.

Figure 4.5 Example Showing How Containers Improve Isolation and Security

SAP HANA 1.0 Database

Classical DB Schema

T1 (Table)

Schema 2

T2 (Table)

Schema 1

T3 (Table)

CV1(Calculation View)


SAP HANA 2.0 Database HDI ContainerC1 (HDI Container)

Classical DB Schema C2 (HDI Container)

T1 (Table) T2 (Table)S2 (Synonym)S1 (Synonym)

T3 (Table)

with user-provided service with HDI service



Role C2::access


If we try this in HDI, we get authorization errors. Because the three schemas are all

fully isolated in three different containers, you’ll have to look at the security when

designing a scenario like this to also enforce loose coupling between different con-

tainers in microservices architectures.

The bottom half shows that to get this working, you have to create a synonym for

each external table you want to use. Each HDI container also has a default access

authorization role that gets created when you first create the container. You need

to assign this role to the owner of your container (schema). Lastly, if you’re con-

necting to a classical database schema, you also need to provide a service. If you

connect to another HDI container, the SAP HANA or SAP Cloud Platform runtimes

already have such a service for you. We discuss all this in more detail in Chapter 13.




SAP HANA XS is embedded in SAP HANA. SAP HANA can be run on separate cheaper

servers.

The database is integrated with the

application.

The data layer is decoupled.

End users of native SAP HANA applications

are managed inside SAP HANA.

End users are managed from an external IdP

system.

Business users and technical users are

together.

Business users and technical users are

separated.

SAP HANA XS is different from SAP Cloud

Platform.

SAP HANA XSA and SAP Cloud Platform

share the same PaaS layer using Cloud

Foundry. Both use a microservices architec-

ture with containers. You can deploy the

same models and code to either platform.

All code and information models are in one

SAP HANA system.

Each application’s services are in its own

container, and SAP HANA information mod-

els are in HDI containers.

All database objects are together in one

SAP HANA database.

Each project and HDB module’s database

objects are isolated in its own HDI container.

Explicit references are used to tables in

other schemas.

Synonyms, authorizations (role assigned),

and possibly a user-provided service are

needed.

XS JavaScript is used. Node.js is used.

Table 4.2 Changes in the Architecture from SAP HANA 1.0 to SAP HANA 2.0


Now that you understand the new modeling and development process, and you

know how all the new features fit into the architecture, we can start using SAP

HANA 2.0.

Creating a Project

Your first step in the modeling and development process will be to create a devel-

opment space. You’ll then need to perform other steps, from creating a develop-

ment user to selecting a space and converting a module.

Creating a Development Space

In Figure 4.2, you saw four different spaces: one for development, one for CI, one

for QA (staging), and another for production. In the traditional SAP world, these

would be called “systems” or “instances.” A space contains the SAP HANA runtime

with its containers and runtime objects.

The concept of “spaces” come from Cloud Foundry, on which SAP HANA XSA and

SAP Cloud Platform are based. Figure 4.6 shows how organizations and spaces are

structured in Cloud Foundry environments. The “n” indicates, for example, that

each Region can have many Global accounts.

In SAP HANA XSA, we look at the Organization and Space levels. Each organization

can have many spaces. The default spaces in a standard Cloud Foundry installation

are development, test, and production. You can create organizations and spaces

using the SAP HANA XSA cockpit tool, also known as the xsa-cockpit tool.

Note

The name of this tool was changed in SAP HANA 2.0 SPS 03. It used to be called the SAP

HANA XSA Administration Tool (xsa-admin). Don’t confuse this new name with the SAP

HANA cockpit, which we’ll discuss in the next section.

The SAP HANA cockpit is mostly used by system and database administrators, whereas

the SAP HANA XSA cockpit is mostly used by modelers and developers.

To find the xsa-cockpit tool, first open the SAP HANA XSA services menu in your

browser at https://<server>:3<instance-number>30. The host (server) name for the

SAP HANA express edition is hxehost, and the instance number is 90. You’ll there-

fore use https://hxehost:39030. The SAP HANA XSA services menu is shown in

Figure 4.7. The xsa-cockpit tool is the eighth option on the right-side menu.


Figure 4.6 Cloud Foundry Organizations and Spaces

Figure 4.7 SAP HANA XSA Services Menu

Cloud Foundry Environment

User account Home

Region

Global Account

Subaccount

Space

Organization

Cloud Foundry applications

Cloud Foundry services

SAP Cloud Platform


Tip

You can also find SAP Web IDE for SAP HANA (webide) and the SAP HANA Interactive Edu-

cation (SHINE) demo (web) on the SAP HANA XSA services menu.

After you’ve opened the xsa-cockpit tool, you’ll see the screen as shown in Figure

4.8. You can click on the New Organization button to create a new organization.

This example shows the HANAExpress organization for SAP HANA, express edi-

tion.

Figure 4.8 SAP HANA XS Advanced Cockpit to Add a New Organization

Note

The UI of the SAP HANA XSA cockpit was changed from the SAP Fiori launchpad look-and-

feel to that of the new SAP Cloud Platform.

Figure 4.9 shows the details of the HANAExpress organization screen. The right side

shows the Spaces. You can add a new development space for your SAP HANA mod-

eling by clicking on the New Space button.

Tip

It’s recommended that you create a development space, instead of using the default SAP

space. In SAP HANA, express edition, the development space is already created for you.


Figure 4.9 Adding a New Space to SAP HANA XSA

Creating a Development User

After you have a development space, you then need a development user in that

space. You can create a new user by selecting the User Management menu option

shown on the left side of the screen in Figure 4.8, and then clicking on the New

User button.

After you’ve created your user, select the Organizations menu option, click on the

HANAExpress space, and select the Development space. Then select the Members

option on the menu. You should see that the XSA_ADMIN and XSA_DEV users are

already in this space. Click on the Add Members button, select your user, select the

Space Developer checkbox, and click the OK button. Your user is now added to the

Development space.

You can also create users in the SAP HANA cockpit. The SAP HANA cockpit is used

by system administrators to manage the SAP HANA system. This replaces the

Administration perspective previously found in SAP HANA Studio. The SAP HANA

cockpit also appears on the SAP HANA XSA services menu as hana-cockpit, as

shown previously in Figure 4.7.

If you want to manually find the browser address for the SAP HANA cockpit (or

other SAP HANA services), you need to log in to the operating system with the


<System ID>adm user. The system ID for SAP HANA, express edition is hxe, and

you need to log in with the hxeadm user. On the command line, type “xs apps”.

You’ll see a list of SAP HANA XSA services, as shown in Figure 4.10.

Figure 4.10 Running “xs apps” on the Command Line to Find Application URLs

To find the SAP HANA cockpit, look for a service called cockpit-web-app. In this

case, the browser address (top line) is https://hxehost:51046. This will probably be

different on your system.

If the service shows as STOPPED in the second column, you can start it by going to

the xsa-cockpit tool, and selecting the Applications menu option, as shown in

Figure 4.11. You’ll then see the list of services. Here you can start any services that

were stopped.

You can create users and assign roles to the users using the SAP HANA cockpit.

We’ll discuss this in Chapter 13.

Figure 4.12 shows what the SAP HANA cockpit looks like. Most of the functionality

in the SAP HANA cockpit is for system administrators, so it won’t be discussed

here.


Figure 4.11 Starting SAP HANA XSA Applications in the SAP HANA Applications Monitor

Figure 4.12 SAP HANA Cockpit Used for Administration


Project Creation Options

You can now open the SAP Web IDE for SAP HANA (webide) tool, which is the main

tool we’ll use for modeling and development.

Even though the names are similar, SAP Web IDE for SAP HANA isn’t the same as

the SAP Web IDE that is used for developing SAP Fiori apps.

When you open SAP Web IDE for SAP HANA, you’ll have an empty Workspace

where you’ll create SAP HANA projects. Each project will be for an SAP HANA appli-

cation, for which you create development and modeling objects.

SPS 04

From SAP HANA SPS 04, you can have multiple workspaces in SAP Web IDE for SAP HANA.

Tip

The following three words sound similar but refer to completely different topics, so it’s

important not to confuse them:

� Space

Space refers to a development or production system.

� Workspace

Workspace refers to your working area in SAP Web IDE for SAP HANA, where you create

projects with development and modeling objects.

� Namespace

Namespace is used for naming development and modeling objects, and it helps deter-

mine the name of the runtime objects in the containers.

There are three ways to create a new SAP HANA project. The following are shown

in Figure 4.13:

1. The first is to right-click on Workspace, and select New from the menu. This cre-

ates a blank project. You then create an SAP HANA MTA Project.

2. The second is to Import an existing project. You need to have previously

exported a project. We’ll look at this a bit more.

3. Lastly, you can Clone a project from the Git repository.


Figure 4.13 Creating a New Project, Importing a Project, or Cloning a Project

A project can have several modules. These will appear as folders in SAP Web IDE for

SAP HANA. Each module can be a different programming language (for application

containers) or SAP HANA database objects (for HDI containers). Some of the mod-

ules you can add are as follows:

� HDB module

These modules contain SAP HANA database objects, data models, data process-

ing (e.g., functions and procedures), virtual tables, synonyms, analytic privi-

leges, CDS views, table data, and tables. As this is the area we’ll focus on, we’ll

discuss these in the rest of the book.

� Java or Node.js modules

These modules are for business logic, written in Java or JavaScript.

� Basic HTML5 module

These modules are for web-based UIs.

� SAPUI5 HTML5 module

These modules are for UIs based on the SAPUI5 standard.


You create your development and modeling objects in the modules (folders) in

SAP Web IDE for SAP HANA. The structure of a project can be described as a collec-

tion of modules. For exam purposes, the most important of these are the HDB

modules. When you build a module, it will create a container after the first success-

ful build. Any subsequent build will create or update the runtime objects in the

container. A module is a collection of design-time objects (files). During a build,

the runtime objects in the container get created from the design-time objects in

the module (folder).

The mta.yaml file, shown in Figure 4.14, contains the configuration for your proj-

ect. Each module is described in this file. The Code Editor view is shown here. There

is also an MTA Editor view that shows the configuration graphically.

Figure 4.14 The mta.yaml File Shows the Project with Its Modules


Selecting a Space for Running the Project

After creating, importing, or cloning a project, you have to tell SAP Web IDE for SAP

HANA which space is your development space before you can build the project

(application).

Right-click on the project, and choose Project • Project Setting from the context

menu. You’ll get a screen like the one shown in Figure 4.15. Select your Space from

the dropdown list, and click on the Save and the Close buttons at the bottom.

Note

You’ll only see spaces in the dropdown list where your user is registered as a developer.

We described how to do this at the start of this section.

Figure 4.15 Select a Space for Running the Project

Converting a Module after an Import

If you’ve imported or cloned a project, you have to do an additional step before

you can run a build. After the import or clone, the project doesn’t yet know the

module types for the various modules because you could have modified the files

in a code editor before importing or cloning.

You therefore have to identify the various module types. You do this by right-click-

ing on a module, choosing Convert from the context menu, and then selecting the

module type, for example, HDB Module (see Figure 4.16).


Figure 4.16 Converting a Module to an HDB Module after Import or Clone



Creating SAP HANA Design-Time Objects

After you’ve created the project and the modules, you can now create your SAP

HANA information and data objects in the HDB module. (Remember that we won’t

discuss the application modules; instead, we’ll focus on the HDB module.)


SAP HANA Studio: Content area and

developer perspective

SAP Web IDE for SAP HANA

SAP HANA Studio: Administration

perspective

SAP HANA cockpit

Development system Development space

Table 4.3 Changes in the Modeling Tools from SAP HANA 1.0 to SAP HANA 2.0


In SAP Web IDE for SAP HANA, the modules appear as folders. The SAP HANA infor-

mation and data objects appear as files in these folders.

SAP Enterprise Architecture Designer

First, we’ll quickly introduce SAP Enterprise Architecture Designer (SAP EA

Designer). As the name indicates, it’s a tool that you use for designing enterprise

architecture diagrams and models during the planning and design phase of an SAP

HANA project. You can visually create data models and views in this tool. This is

one of the ways to create SAP HANA design-time objects.

You can also use it to reverse-engineer existing SAP HANA information views. In

Figure 4.17, we used the Reverse HDI Files option to create an enterprise architec-

ture diagram of an existing SHINE calculation view. You can use this feature to

help you in migration projects from other databases to SAP HANA or SAP Cloud

Platform. As it can create the required CDS artifacts used by the application pro-

gramming model for SAP Cloud Platform, this functionality can save you a lot of

time. (We’ll discuss CDS in the Creating a Table Using Core Data Services section.)

You can use SAP EA Designer to get a complete overview of new or existing proj-

ects. It allows you to use the collaborative web-based environment to design a

project’s models, data flows, processes, requirements, and so on.

Figure 4.17 SAP Enterprise Architecture Designer


SAP EA Designer is updated more often than SAP HANA, and new features appear

quite quickly. With SAP HANA SPS 03, we got a new type of table called system-ver-

sioned tables. (We’ll take a quick look at this in Chapter 8.) While there was still lim-

ited documentation about the CDS format for this new table type, we could

already create such tables using the SAP EA Designer tool, and it would create the

required and compliant CDS files for us.

SAP EA Designer provides General Data Protection Regulation (GDPR) support in

data models and can generate the SAP HANA data security features of anonymiza-

tion.

SAP Web IDE for the SAP HANA User Interface

Before we create new SAP HANA design-time objects (files) in SAP Web IDE for SAP

HANA, let’s take a good look around to understand the tool better.

Figure 4.18 shows an annotated overview of a typical screen in SAP Web IDE for SAP

HANA, which is composed of the following:

� A standard menu bar appears across the top of the screen.

� Starting at the left, we see a vertical toolbar with three icons. The first accesses

the Development tool 1, which is the equivalent of the Developer Perspective in

SAP HANA Studio. We use this to create and edit design-time objects.

The second accesses the SAP HANA database explorer 2, which is the equiva-

lent of the Catalog area in SAP HANA Studio. We use this to work with the run-

time objects. We’ll look at the SAP HANA database explorer tool more in the

next section.

� The next area to the right is the Workspace area, which has a folder-like tree

structure. The top folder is the Workspace. Below this we have the project 7 (in

this example, hana-shine-xsa), which is equivalent to an application. The project

folder contains several modules 8, and the modules subfolders contain objects

9 (i.e., files).

� The middle area is the Scenario area k in which we can create and update our

SAP HANA information models. When you open an information model, the

name of the model appears in a tab at the top of this area 4 (in this example, it’s

the PRODUCTS.hdbcalculationview tab). On the left of this area is the palette

toolbar 3, which shows what nodes you can use to create the SAP HANA infor-

mation model.


Figure 4.18 Overview of SAP Web IDE for SAP HANA

Tip

If your space is limited on the screen, you can double-click on the name tab. The window

will expand over the workspace tree structure. If you double-click on it again, it restores

to the smaller size.

� The next area to the right of the scenario area is the details area 5. When you

select a node j in the scenario area, the details of that node will be shown in the

details area. The details area has several tabs showing at the top of the area. In

Figure 4.18, the Join Definition tab is selected.

� Below the details area is the PROPERTIES section l. When you select something

in the details area, the properties of that item are shown here.

� The toolbar on the top-right side 6 has tools for Searching objects, Debugging,

and so on. When you select a tool, an area will expand to the left of the toolbar

to show that tool. Clicking on the icon again will collapse the area.

� The toolbar on the bottom-right side has the Console icon m. This is where you’ll

see messages, for example, build errors. Once again, when you select the Con-

sole icon, an area will expand to the left of the toolbar to show the Console.


When you right-click on the folders in the Workspace tree structure, the context

menu shows a New option. Select the New option, and a list of possible design-

time objects appear (see Figure 4.19).

Figure 4.19 The New Context Menu in SAP Web IDE for SAP HANA

Note

We’ll discuss many of these different file types in the next chapters.

Let’s take a quick look at the HDB CDS Artifacts object from this list.

Creating a Table Using Core Data Services

If you’ve used SAP HANA Studio with SAP HANA 1.0, you’ll remember that you

could create a new table using a table designer tool. This would create the table

directly in the SAP HANA database as a runtime object. The problem was that such

tables could not be deployed (transported) to the production system.

SAP HANA introduced the concept of CDS in the last few versions of SAP HANA 1.0.

We’ll discuss CDS in detail in the next chapter.

Basically, CDS files are design-time files that can be deployed on multiple systems.

You’ll use an .hdbcds files to define your SAP HANA database table. The syntax of


the CDS files is very close to standard SQL syntax. To create the actual table in the

database—the runtime object—you first have to build the CDS file.

The SHINE project is a good example of CDS data sources in action. You don’t have

to create an HDI container, create tables, or import data into these tables. When

SHINE is imported into your SAP HANA system, and you start a build, it automati-

cally creates all that content for you using CDS files.

Figure 4.20 shows how to create an .hdbcds file. In this example, the SAP HANA CDS

file is used to create two tables called MEMBERS and RELATIONSHIPS. You can Save the

file as shown 1 or by pressing (Ctrl)+(S) (if you use Microsoft Windows).

Figure 4.20 Creating and Saving an .hdbcds File to Design a Table


Documentation

As you near the end of a modeling project, documentation becomes important.

You need to hand your modeling work over to someone else. There are always

good reasons for the many decisions you make, such as why the various nodes of

a calculation view are in a specific order. Documentation tools and features in SAP

HANA, such as notes, allow you to communicate that information to anyone who

may work on your project in the future.

In the context menu of a calculation view (see Figure 4.21), you’ll find a very useful

documentation feature. The Generate Document option documents graphical cal-

culation views in SAP HANA. Figure 4.21 shows an example of the HTML output

generated. You can select an entire module (folder), and it will generate the HTML

documents for all of your calculation views in one go.

Figure 4.21 Generating Documentation of SAP HANA XSA Calculation Views




Building and Managing Information Models

When you build an entire project the first time, and the build is successful, it will

create the various runtime containers. However, you won’t find an HDI container.

It turns out that SAP HANA requires a separate dedicated build of each HDB mod-

ule to create the corresponding HDI containers.

When an HDI container is created, it has a corresponding physical schema for the

container with a long technical name.

When you’ve successfully built an HDB module design-time file, you’ll have a run-

time object in the HDI container. You can work with these runtime objects using

the SAP HANA database explorer tool. You find this tool at the second icon on the

left toolbar in SAP Web IDE for SAP HANA.

The top-left area of Figure 4.22 shows the various types of runtime objects in the

container. Column Views are the runtime versions of SAP HANA views that were

created with the build process. Another group that you’ll look at very often is

Tables.

Remember

You create SAP HANA database design-time objects in the HDB module (folder). When

you successfully build the HDB module the first time, it creates an HDI container. SAP

HANA uses the design-time objects (files) in the HDB module to create runtime objects in

the HDI container.


Create design-time objects in SAP HANA

Studio in the developer perspective

Create design-time objects in application

modules and HDB modules

SAP HANA Studio: Catalog area SAP HANA database explorer

Create tables in SAP HANA Studio using

a table designer tool

Create tables using .hdbcds files and

building the files

Use of .hdbtable for creating tables

using CDS

Use of .hdbcds for creating tables using CDS

Table 4.4 Changes in Creating Objects from SAP HANA 1.0 to SAP HANA 2.0


Table Definitions and Data

When you select the Tables group, it shows a list of all the tables for the HDB mod-

ule in the bottom left area of the screen (see Figure 4.22).

To open the definition of a table on the right side, either double-click a table’s

name or right-click and select Open from the context menu.

Figure 4.22 SAP HANA Database Explorer Showing a Table Definition

Data Preview

You can view and maintain table data when you select Open Data from the table’s

context menu in the SAP HANA database explorer.

You can even insert data and update data while you preview the table contents, as

illustrated in Figure 4.23. This obviously can be dangerous in a production system,

so the action can be prevented through security settings.

SPS 04

One of the features that you could do with SAP HANA Studio in SAP HANA 1.0 was to

import data into a table from CSV files or Microsoft Excel spreadsheets. One way you

could do it in SAP HANA 2.0 was by defining a CDS .hdbtabledata file and specifying the

CSV file. With SPS 04, you can now import data from medium-sized CSV files or Excel

spreadsheets using the SAP HANA database explorer.


In Figure 4.23, you see two different tabs when previewing the data. The Raw Data

tab shows the raw data that can be filtered, sorted, and exported. In the Analysis

tab, you can perform a full analysis of table outputs or information model outputs.

This works the same as an Excel pivot table. You can show the data using many dif-

ferent types of graphs—for example, the heat map graph shown in Figure 4.24.

Figure 4.23 Displaying Table Data with the Option to Insert Data

Figure 4.24 Data Preview Analysis of an SAP HANA Information Model


SQL Console

In the Raw Data tab, you also have the Edit SQL Statement in SQL Console option,

which opens a SQL statement for viewing the table or viewing data in the SQL Con-

sole tool.

You can now modify the SQL statement and see the results below it, as shown in

Figure 4.25.

Figure 4.25 Data Preview of an SAP HANA Information Model

Note

Never use the SQL Console to create database objects. You can use it to observe changes

in reading data and for opening the Analyze SQL tool. We discuss this very useful tool in

Chapter 12.

You have to create SAP HANA database objects using CDS.

If you create an SAP HANA database object using the SQL Console, only the run-

time version of the object will exist in the current HDI container. No design-time

object will exist.


When you build and deploy your HDB module/HDI container to another space,

the build and deployment will be successful. The object you created using the SQL

Console will, however, be missing from that space because only the design-time

objects get built and deployed.

If you wanted to create objects using the SQL Console, you would have to manually

do it the same way in every space (system). Another problem with doing this is

that someone needs similar security access to create it in the production environ-

ment, which is a huge security risk.

Naming Runtime Objects

When you build a design-time object, the build process creates the runtime ver-

sion of that object. The question we ask now is, “What is the name of that runtime

object in the container?” This is important to know, as this is the name that analyt-

ics or reporting users need to use.

The intuitive assumption is that the file name of the design-time object will be the

name of the runtime object. For most objects, that assumption will work, but it

won’t work for all objects. If you look at Figure 4.20 again, you’ll see that you create

two tables with one .hdbcds design-time definition file. We can’t give both tables

the same name. Therefore, runtime objects are named as <namespace>::<object-name>.

We mentioned namespaces before when we said there was a difference between a

space, a workspace, and a namespace. A namespace is used when naming develop-

ment and modeling objects, and it helps determine the name of the runtime

objects in the containers.

By default, the namespace is blank. If you have a design-time object called

View1.hdbcalculationview, the default name for the runtime object will be ::View1.

Note

The runtime object name is the same as the file name of the design-time object, without

the file extension, for example, .hdbcalculationview.

Because the runtime object names must be unique, you shouldn’t have View1.hdbcalcu-lationview and View1.hdbfunction in the same folder as both will generate runtime

objects with the name ::View1.

In a small project, there is probably not much to be gained by specifying a name-

space. In larger projects, or when developing libraries and application program-

ming interfaces (APIs), you get to the situation where some objects can have the


same name. In that case, you start adding a namespace to differentiate which spe-

cific object you’re talking about. The namespace also gives a sense of where it fits

in. For example, you can have two objects with the name Show. By adding different

namespaces, you could get Report::Show and Users::Show. The namespace tells

other modelers and developers that the context is either Reports or Users.

Note

The folders don’t have to be named Report and Users. The folder names aren’t related to

the namespace at all.

The combination of namespace and object name should be unique. You can have

two objects called Show, provided they are in different namespaces as shown previ-

ously.

Tip

Think of the namespace like a surname. Many people are called Mark. By adding a sur-

name, it becomes easier to identify which person called Mark we refer to, for example,

Mark Green.

When you create a new HDB module, an .hdinamespace file gets created inside the

source (src) subfolder of the module. This file contains the namespace for the files

in that folder. The default namespace is blank, as shown by the name entity in Lis-

ting 4.1.

{"name": """subfolder": "ignore"

}

Listing 4.1 The Default.hdinamespace File for an HDB Module

The subfolder entity indicates whether this namespace is applicable to the sub-

folders as well or whether the subfolders should ignore this namespace. You can

specify cascade instead of ignore to make the namespace applicable to the subfold-

ers as well.

Because there is one .hdinamespace file in a folder, it means that all the objects in

that folder will have the same namespace. If you want to give some objects

another namespace, you’ll have to put them in a subfolder that contains a differ-

ent .hdinamespace file with another namespace specified.


You can also specify the namespace in a design-time file, but then it must corre-

spond to the namespace specified in the .hdinamespace file. Conflicts will cause

build errors. Remember the namespace is specified by the .hdinamespace file in

the same folder or by an .hdinamespace file in the parent folder with cascading

enabled.

Tip

If no .hdinamespace file is found, or if the .hdinamespace file is empty, the namespace is

assumed to be blank.

We’ve mentioned that by default the file name of the design-time object deter-

mines the name of the runtime object. The exception to this is where we have

design-time files in which many objects are specified in a single file. We already

saw an example in Figure 4.20, where we created two tables—MEMBERS and RELA-

TIONSHIPS—with the graphf.hdbcds design-time definition file. If our namespace is

specified as tables.graph, the runtime objects will be called tables.graph::MEMBERS

and tables.graph::RELATIONSHIPS. In this case, the file name graphf is ignored.

Synonyms are another example. You can create many synonyms in a single .hdb-

synonym file.

Tip

When you need to access an external data source, you must first define a local synonym

in an .hdbsynonym file. You might also need to create or update the .hdbgrants (“permis-

sions”) file to ensure that the external data source can actually be accessed.

Build Errors

You know by this time that you need to build the design-time file to create the run-

time files. End users use the runtime files (refer to the top-right side of Figure 4.2).

When you’re developing or modeling SAP HANA information views, you only

have the design-time files. Until you do the build, no one can run these views.

Tip

When you want to view the results of a calculation view, you need to first build the view.

If you make some changes, save the view, and then ask for a preview of the data without

building it first, you’ll get the results from the previous build—without your latest

changes.


In a book, it’s easy to show you the build menu and assume that all will go well. But

in real life, you’ll occasionally get build errors. You need to know why you get the

errors because that will help you figure out how to solve them. We’ll examine

some examples of build errors in the following.

Figure 4.26 shows a build error in the console window at the bottom. The high-

lighted error message reads no measure or attribute present. In this case, we

haven’t yet mapped the input fields to the output fields, and the view had no out-

put fields in the top layer. We’ll look more at how to create this type of view in

Chapter 6.

Figure 4.26 A Build Error

Sometimes the build messages aren’t that clear. Then you’ll have to understand

which other objects your view may depend on.

Let’s look at an example where you use a table from another schema in your view.

When you build only your view—not the entire HDB module—you might get a

build error. When you’re using a table from another schema, you already know

that you need a synonym and an authorization assigned. You might also need a

user-defined cross-schema service if the table is from a classic database schema. If,


for example, the synonym isn’t yet built, you’ll get a build error. You could also get

build errors because you don’t have the authorization, the service hasn’t been

deployed yet, or even that the other schema and tables haven’t yet been created in

the space (system) where you’re doing the build.

Build errors can really get tricky when you start renaming or deleting objects.

Sometimes you can introduce build errors when you refactor your code or models.

Refactoring

Refactoring is the process of restructuring your information models without

changing their external behavior (i.e., their result sets). You do this to improve,

simplify, and clarify the design of your models, for example, when you move

information models inside the system from one module to another or when you

rename an information model.

Renaming or moving an information model isn’t always a trivial task. If the infor-

mation model is used in many other models, you’ll have to update those models

as well. You can easily find yourself having to update tens of information models

when simply trying to rename a single information model that these models all

use.

Concepts such as refactoring are used in larger projects to ensure your modeling

work stays fresh and useful. The idea is similar to gardening: you need to mow the

lawn, pull weeds, and prune the roses to maintain a beautiful garden. This idea of

upkeep can also be applied to the administration of your data models.

We’ll start by looking at some of the refactoring features in SAP HANA. After that,

we’ll look at some examples of what to watch out for so that you don’t break your

models.

Where-Used

In the refactoring process, you may have to figure out where a specific information

model is used. Figure 4.27 shows the context menu of an information model in the

SAP HANA database explorer. By selecting the Where-Used Browser menu option,

SAP HANA finds all the places where that information model is used. This is very

useful to see the impact a planned refactoring will have.


Figure 4.27 Where-Used Browser Option for Finding Where an SAP HANA Information Model Is Used

Replacing Data Sources and Nodes in an SAP HANA View

Figure 4.28 shows two context menus of nodes in a graphical calculation view. In

this case, you can replace the data sources used in the node or even replace or

delete the entire node.

Figure 4.28 Replacing Data Sources and Nodes in an SAP HANA View


You normally won’t do this for an information view that is in use because the

result set of the calculation view will change. But replacing the data source in a

node is useful when you have to create different calculation views that are based

on similar data tables. You can make a copy of an existing view and then change

the data source to a similar table. SAP HANA will ask you to verify the mapping of

the old data source to the new data source. It will then keep as much of the current

information intact as possible, which can save you many hours of building similar

calculation views.

Show Lineage in an SAP HANA view

Sometimes you have renamed a column name in an SAP HANA view. When you

want to find out from which table the column came, you can’t just search for the

column names in the tables as the column was renamed inside the view. In that

case, you can use the Show Lineage option.

Select an output column, for example, Supplier_Country from the Name column

shown in Figure 4.29, and click on the Show Lineage icon.

Figure 4.29 Show Lineage Option Showing the Data Source of a Column


Go down the layers of nodes, and you can see that the Supplier_Country column

was introduced by the Addresses table in the second (from the bottom) join node.

This works, even if the column got renamed along the way.

Avoiding and Fixing Errors as a Result of Refactoring

We end our journey into the new world of SAP HANA 2.0 by looking at some exam-

ples where the build process gives errors when you might not expect them. By

looking at why this happens, you’ll also understand the build process better.

For our first example, we look at a case with two information models called A and

B. We start by creating Model A, and then we create Model B based on Model A. We

know that we have to build Model A first because Model B depends on it.

When we’ve successfully built both models, we rename a field inside Model A. This

field is used in Model B. When we build the models we expect Model A to build suc-

cessfully as the field was just renamed, and this won’t break the model. We expect

the build of Model B to give an error that it can’t find the field it expects from

Model A.

In this case, our expectation is wrong. The build of Model A will give an error. SAP

HANA does an end-to-end check when you do the build, even if you just ask it to

only build Model A. It sees that Model B depends on Model A and that one of the

fields that Model B requires from Model A isn’t there anymore. Because Model A is

now breaking the “contract” it has with Model B, the build of Model A will give an

error.

Remember

SAP HANA does an end-to-end check when doing a build, even if you just ask it to build a

single model.

Instead of renaming a field in Model A, what happens if we rename Model A to

Model E? In this case, the build of Model E (the new name) will be successful, but

the build of Model B, or the build of the entire HDB module (Model B and Model E),

will fail.

Even though the build process does end-to-end dependency checks, you broke the

link between Model A and Model B by renaming Model A to Model E. Model E is

now independent. Model B is based on Model A, which now doesn’t exist, so the

build gives an error.


Tip

Use the Where-Used option to find which models will “break” before you rename an SAP

HANA information model.

Deleting objects can cause weird effects. Coming back to our example of Model A

and Model B, where Model B is based on Model A, let’s assume we’ve successfully

built both models. The runtime objects for both models exist in the HDI container.

What happens when we delete Model A? When we make a change to Model B, and

rebuild it, we expect the build to fail. But the build succeeds. This is because SAP

HANA does an incremental build. It looks at what needs to be rebuilt. It sees Model

A’s runtime object in the HDI container and sees that it doesn’t need a rebuild, so

it just rebuilds Model B.

If we now do a build of the entire HDB module, it finally realizes that Model A is

deleted and then gives a build error.

Hint

After deleting files from the HDB module, do a build of the entire module.

If you ever want to delete an entire folder or subfolder, don’t just delete the mod-

ule (folder). First delete the files in the folder, and do a build of the module (folder).

Then you can delete the folder.

If you first delete the files, the build process realizes that the design-time files have

been deleted, and it then deletes the runtime objects from the HDI container. If

you delete the entire module (folder), you can’t trigger a build any more, and the

runtime objects won’t get deleted from the HDI container.


In this chapter, we covered the following important terminology:

� Design-time objects

These objects are definitions (metadata) for what SAP HANA should create

when running information models. They are seen as the “inactive” versions,

and end users can’t run them.

� Runtime version

When you build an information model, SAP HANA checks the model and cre-

ates an “active copy” of the information model in the container (schema). This

active copy is called a runtime version.


� Container

An isolated miniature virtual machine that contains runtime objects.

� Build

The process of creating a runtime object from the design-time files.

� Deploy

The process of distributing your development and modeling work to other sys-

tems, for example, production.

� Git

A powerful open-source repository of design-time objects that allows for ver-

sion control, forking, and merging of branches.

� SAP Web IDE for SAP HANA

The web-based environment used for creating SAP HANA design-time objects.

� SAP HANA database explorer

The web-based tool used to work with SAP HANA runtime objects.

� HDB module

A collection of SAP HANA database design-time objects

� HDI container

The container and schema created when you build an HDB module.

� Refactoring

The process of restructuring your information models without changing their

external behavior to improve, simplify, and clarify the design of your models.

Practice Questions




on the exam itself, they will allow you to review your knowledge of the subject.

Select the correct answers, and then check the completeness of your answers in

the “Practice Question Answers and Explanations” section. Remember that on the

exam, you must select all correct answers, and only correct answers, to receive

credit for the question.


1. What development tool is available for creating SAP HANA XSA and HDI infor-

mation models?

� A. SAP HANA Studio

� B. SAP HANA Web-Based Development Workbench

� C. SAP HANA database explorer

� D. SAP Web IDE for SAP HANA

2. True or False: CDS only works with runtime objects.

� A. True

� B. False

3. Refactoring is used with which of the following actions?

� A. Improving your modeling design

� B. Translating

� C. Deployment

� D. Documenting

4. For what tasks would you use the Where-Used option in SAP HANA?

� A. To find all places where an information model has been deployed

� B. To find all places where a developer updated information models

� C. To find all places where an information model is used

5. You have Model B that is based on Model A. You have NOT built any of the

models yet. You delete Model A. What happens during the build? (There are 2

correct answers.)

� A. You get a build error when you build Model B.

� B. The build of Model B is successful.

� C. You get an error when you build the entire HDB module.

� D. The build of the entire HDB module is successful.


6. You have Model B that is based on Model A. You have successfully built both

models. You delete Model B. What happens during the build? (There are 3 cor-

rect answers.)

� A. You get a build error when you build Model A.

� B. The build of Model A is successful.

� C. You get an error when you build the entire HDB module.

� D. The build of the entire HDB module is successful.

� E. Model B gets deleted from the HDI container.

7. What should you use to create tables in an HDI container? (There are 2 correct

answers.)

� A. SQL console

� B. Core data services (CDS)

� C. SAP HANA database explorer

� D. SAP Enterprise Architecture Designer

8. What do you need to read a table from another HDI container in your infor-

mation model? (There are 3 correct answers.)

� A. A user-defined service

� B. Authorization

� C. Synonym

� D. An HDI service

� E. Authentication

9. What do you need to do after creating a new project with an HDB module?

� A. Create an .hdinamespace file

� B. Convert the new module to an HDB module.

� C. Select a space for running the project.

10. True or False: You can use the Show Lineage tool to find out which model

another model is based on.

� A. True

� B. False




You can use SAP Web IDE for SAP HANA to create SAP HANA XSA and HDI infor-

mation models.

SAP HANA Studio and SAP HANA Web-Based Development Workbench were

used with SAP HANA 1.0 and SAP HANA XS. SAP HANA XS has been deprecated

from SAP HANA 2.0 SPS 02. These older tools don’t have functionality to work

with HDI containers.

The SAP HANA database explorer can show HDI container objects but doesn’t

create them.


False—CDS objects are design-time objects. They create runtime objects when

they are activated. The word “only” makes this statement false.

Tip

Remember that simple true or false questions don’t occur in the certification exam. The

other questions with at three or four answers are more representative of the questions

you’ll find in the certification exam.


Refactoring is used to improve your modeling design. You can use it, for exam-

ple, to rename SAP HANA models.

Translating is used to translate your models to other languages (e.g., Chinese).

Deployment is taking your modeling work from the development system to

the production system.

Documenting is writing documents and explaining your modeling work.


You use the Where-Used option in SAP HANA to find all the places an informa-

tion model is used.


You have Model B that is based on Model A. You have NOT built any of the mod-

els yet. You delete Model A: You get a build errors when you build Model B and

the entire HDB module because Model B depends on Model A. You get an error

on Model B because you haven’t yet built either one of the models.



You have Model B that is based on Model A. You have successfully built both

models. You delete Model B: The build of both Model A and the entire HDB

module is successful.

Model B’s runtime object also gets deleted from the HDI container. You only

deleted the design-time object from the HDB module. The build deletes the

runtime object.


You should use CDS and SAP Enterprise Architecture Designer to create tables

in an HDI container.

You should never use SQL Console to create tables as they can’t be deployed

(transported).

You can’t use SAP HANA database explorer to create tables.

8. Correct answers: B, C, D

You need authorization, a synonym, and an HDI service to read a table from

another HDI container in your information model.

You need a user-defined service when reading a table from a classic database

schema, not from another HDI container.

Authentication is your login credentials, such as a user name and password. Do

not confuse this with authorization, which signifies what you’re allowed to do.


You need to select a space for running the project after creating a new project

with an HDB module.

An .hdinamespace file is created automatically when you create an HDB mod-

ule.

You only have to convert the new module to an HDB module after an import.


False. You do NOT use the Show Lineage tool to find out which model another

model is based on. You can use it to find out where a specific column in an

information view originates from.


Takeaway

You should now understand the SAP HANA 2.0 development and modeling pro-

cess and architecture in more detail. After reading this chapter, you should be able

to create a project, create and build new SAP HANA information models, refactor

your modeling work, and solve build error situations.

In addition, you now should have a working knowledge of what happens behind

the scenes when you create and build a model.

Summary

In this chapter, we discussed the SAP HANA modeling tools: SAP Web IDE for SAP

HANA, SAP HANA database explorer, SAP HANA cockpit, SAP EA Designer, and the

SAP HANA XSA cockpit. You know how they work and when to use them.

You can now use this knowledge for the modeling chapters ahead. The next chap-

ter will teach you the fundamentals of modeling.

Chapter 5

Information Modeling Concepts


� Understand general data modeling concepts such as views,

cubes, fact tables, and hierarchies

� Know when to use the different types of joins in SAP HANA

� Examine the differences between attributes and measures

� Learn about the different types of information views that

SAP HANA uses

� Get to know projections, aggregations, and unions used in

SAP HANA information views

� Describe core data services (CDS) in SAP HANA

� Get a glimpse of best practices and general guidelines for data

modeling in SAP HANA

Chapter 5 Information Modeling Concepts182

Before moving onto the more advanced modeling concepts, it’s important to

understand the basics. In this chapter, we’ll discuss the general concepts of infor-

mation modeling in SAP HANA. We’ll start off by reviewing views, join types,

cubes, fact tables, hierarchies, the differences between attributes and measures,

the different types of information views that SAP HANA offers, and CDS views.

From there, we’ll take a deeper dive into SAP HANA’s information views, as well as

concepts such as calculated columns, how to perform currency conversions, input

parameters, and so on.

Real-World Scenario

You start a new project. Many of the project team members have some

knowledge of traditional data modeling concepts and describe what they

need in those terms. You need to know how to take those terms and ideas,

translate them into SAP HANA modeling concepts, and implement them in

SAP HANA in an optimal way.

Some of the new concepts in SAP HANA are quite different from what you’re

used to, for example, not storing the values of a cube, but instead calculating

it when required. If you understand these modeling concepts, you can

quickly understand and create real-time applications and reports in SAP

HANA.


The objective of this portion of the SAP HANA certification is to test your under-

standing of basic SAP HANA modeling artifacts.

For the certification exam, SAP HANA modelers must have a good understanding

of the following topics:

� The different types of joins available in SAP HANA and when to use each one

� How the SAP HANA information views correspond to traditional modeling arti-

facts

� The different types of SAP HANA views and when to use them

� The purpose of CDS in SAP HANA


Note



This section looks at some of the core data modeling concepts used when working

with SAP HANA and that will be covered on the exam. Let’s start from where the

data is stored in SAP HANA by looking at tables.

Tables

Tables allow you to store information in rows and columns. Inside SAP HANA,

there are different ways of storing data: in row-oriented tables or column-oriented

tables. In a normal disk-based database, we use row-oriented storage because it

works faster with transactions in which we’re reading and updating single records.

However, because SAP HANA works in memory, we prefer the column-oriented

method of storing data in the tables, which allows for the use of compression in

memory, removes the storage overhead of indexes, saves a lot of space, and opti-

mizes performance by loading compressed data into computer processors.

Next, we can begin combining the tables in SAP HANA information models.

Views

The first step in building information models is building views. A view is a database

entity that isn’t persistent and is defined as the projection of other entities, such as

tables.

For a view, we take two or more tables, link these different tables together on

matching columns, and select certain output fields. Figure 5.1 illustrates this pro-

cess.


Figure 5.1 Database View

There are four steps when creating database views:

1. Select the tables.

Select two or more tables that have related information, for example, two tables

listing the groceries you bought. One table contains the shop where you bought

each item, the date, the total amount, and how you paid for your groceries. The

other table contains the various grocery items you bought.

2. Link the tables.

Next, link the selected tables on matching columns. In our example, perhaps

our two tables both have shopping spree numbers, which should ideally be a

unique number for every shopping trip you took. We call this a key field. You can

link the tables together with joins or unions.

3. Select the output fields.

You want to show a certain number of columns that are of interest, for example,

to use in grocery analytics. Normally, you don’t want all the available columns

as part of the output.

Selected field 1 Join

Left Table Right Table

Foreign key

Primary key

Selected field 2

Selected field 3

View

Selected field 1

Selected field 2

Selected field 3

Result of the view looks like a table


4. Save the view.

Finally, save your view, and it’s created in the database. These views are some-

times called SQL views. You can even add some filters on your data in the view,

for example, to show only the shopping trips in 2019 or all the shopping trips in

which you bought apples.

When you call this view, the database gathers the data. The database view pulls the

tables out, links them together, chooses the selected output fields, reads the data

in the combined fields, and returns the result set. After this, the view “disappears”

until you call it again. Then, the database deletes all the output from the view. It

doesn’t store this data from the view inside the database storage.

Tip

It’s important to realize that database views don’t store the result data. Each time you

call a view, it performs the full process again.

You might have also heard about materialized views, in which people store the out-

put created by a view in another table. However, in our definition of views, we

stated that a view is a database entity that isn’t persistent; that is the way we use the

term view in SAP HANA.

The data stays in the database tables. When we call the view, the database gathers

the subset of data from the tables, showing us only the data that we asked for. If we

ask a database view for that same data a few minutes later, the database will

regather and recalculate the data.

This concept is quite important going forward because you make extensive use of

views in SAP HANA. For example, SAP HANA creates a cube-like view, sends the

results back to you, and “disappears” again. SAP HANA performs this process fast,

avoiding consuming a lot of extra memory by not storing static results. What

really makes this concept important is the way it enables you to perform real-time

reporting.

In the few minutes between running the same view twice, our data in the table

might have changed. If we use the same output from the view every time, we won’t

get the benefit of seeing the effect of the updated data. Extensive caching of result

sets doesn’t help when we want real-time reporting.

Note

Real-time reporting requires recalculating results because the data can keep changing.

Views are designed to handle this requirement elegantly.


We’ve discussed the basic type of database views here, but SAP HANA takes the

concept of views to an entirely new level! Before we get there, we’ll look at a few

more concepts that we’ll need for our information-modeling journey.

Cardinality

When joining tables together, we need to define how much data we expect to get

from the various joined tables. This part of the relationship is called the cardinal-

ity.

You’ll typically work with four basic cardinalities. The cardinality is expressed in

terms of how many records on the left side join to how many records on the right

side. The cardinalities are as follows:

� One-to-one

Each record on the left side matches with one record on the right side. For

example, say you have a lookup table of country codes and country names. In

the left table, you have a country code called ZA. In the right table (the lookup

table), this matches with exactly one record, showing that the country name of

South Africa is associated with ZA.

� One-to-many

In this case, each record on the left side matches with many records on the

right side. For example, say you have a list of publishers in the left table. For

one publisher—such as SAP PRESS—you find many published books.

� Many-to-one

This is the inverse of one-to-many in that many records on the left side match

one record on the right side. For example, this case may apply when many peo-

ple in a small village are all buying items at the local corner shop.

� Many-to-many

In this case, many records on the left side match many records on the right side,

such as when many people read many web pages.

Joins

Before we look at the different types of joins, let’s quickly discuss the idea of “left”

and “right” tables. Sometimes, it doesn’t make much difference which table is on

the left of the join and which table is on the right because some join types are sym-

metrical. However, with a few join types, it does make a difference.


We recommend putting the most important table, seen as the main table, on the

left of the join. An easy way to remember which table should be the table on the

right side of the join is to determine which table can be used for a lookup or for a

dropdown list in an application (see Figure 5.2).

Figure 5.2 Table Used for Dropdown List on Right-Hand Side of a Join

This doesn’t always mean that this table has to be physically positioned on the left

of the screen in our graphical modeling tools. When creating a join in SAP HANA,

the table we start the join from (by dragging and dropping) becomes the left table

of the join.

Databases are highly optimized to work with joins; they are much better at it than

you can hope to be in your application server or reporting tool. SAP HANA takes

this to the next level with its new in-memory and parallelization techniques.

In SAP HANA, there are a number of different types of joins. Some of these are the

same as in traditional databases, but others are unique to SAP HANA. Let’s start by

looking at the basic join types.

Left Table—Invoices

Right Table—Customers

Field 1 Field n Customers

Invoice 1 Customer 1



Customers Field 1 … Field n

Customer 1

Customer 2

Customers

Customer 1

Customer 2

Customers lookup


Basic Join Types

The basic join types that you’ll find in most databases are inner joins, left outer

joins, right outer joins, and full outer joins. You can specify the join types in data-

bases using SQL statements. We’ll discuss SQL in more detail in Chapter 8.

The easiest way to visualize these join types is to use circles, as shown in Figure 5.3.

This is a simplified illustration of what these join types do. (The assumption is that

the tables illustrated here contain unique rows; that is, we join the tables via pri-

mary and foreign keys, as illustrated earlier in Figure 5.1. If the tables contain dupli-

cate records, this visualization doesn’t hold.)

Figure 5.3 Basic Join Types and Their Result Sets

The left circle represents the table on the left side of the join. In Figure 5.3, the left

table contains the two values A and B. The right circle represents the table on the

right side of the join and contains the values B and C.

Right Table

B

C

Left Table

A

B

Right Table

B

C

Left Table

A

B

Right Table

B

C

Left Table

A

B

Inner Join

B B

Right Table

B

C

Left Table

A

B

Full Outer Join

A NULL

B B

NULL C

Left Outer Join

A NULL

B B

Right Outer Join

B B

NULL C


Inner Join

The inner join is the most widely used join type in most databases. When in doubt

and working with a non-SAP HANA database, try an inner join first.

The inner join returns the results from the area where the two circles overlap. In

effect, this means that a value is shown only if it’s found to be present in both the

left and the right tables. In our case, in Figure 5.3, you can see that the value B is

found in both tables. Because we’re only working with the “overlap” area, it doesn’t

matter for this join type which table is on the left or on the right side of the join.

Left Outer Join

When using an inner join, you may find that you’re not getting all the data back

that you require. To retrieve the data that was left out, you use a left outer join.

You’ll only use this join type when this need arises.

With a left outer join, everything in the table on the left side is shown (first table).

If there are matching values in the right table, these are shown. If there are any

“missing” values in the right-hand table, these are shown with a NULL value. For this

join type, it’s important to know which tables are on the left and the right side of

the join.

Right Outer Join

The inverse of the left outer join is the right outer join. This shows everything from

the right table (second table). If there are matching values in the left table, these

are shown. If there are any missing values in the left-hand table, these are shown

with a NULL value.

Full Outer Join

As of SAP HANA 1.0 SPS 11, we have the full outer join. Many other databases also

have this join type, so it’s still regarded as one of the four basic join types. This join

type combines the result sets of both the left outer join and right outer join into a

single result set.

Self-Joins

There isn’t really a join type called a self-join; the term refers to special cases in

which a table is joined to itself. The same table is both the left table and the right


table in the join relationship. The actual join type would still be something like an

inner join.

This configuration is used mostly in recursive tables—that is, tables that refer back

to themselves, such as in HR organizational structures. All employee team mem-

bers have a manager, but managers are also employees of their companies and

have someone else as their manager. In this case, team members and managers

are all employees of the same company and are stored in the same table. Other

examples include cost and profit center hierarchies or bills of materials (BOMs).

We’ll return to this concept when we discuss hierarchies later in this chapter.

SAP HANA Join Types

The join types on the right side of Figure 5.4 are unique to SAP HANA: referential

join, text join, and temporal join.

Figure 5.4 Basic Join Types and Some SAP HANA-Specific Join Types

Inner Join

Left Outer Join

Right Outer Join

Referential Join,Temporal Join

Text Join

Full Outer Join


Referential Join

The referential join type normally returns exactly the same results as an inner

join. So, what’s the difference? There are many areas in which SAP HANA tries to

optimize the performance of queries and result sets. In this case, even though a

referential join gives the same results as an inner join, it provides a performance

optimization under certain circumstances.

The referential join uses referential integrity between the tables in the join. Refer-

ential integrity is used in a business system to ensure that data in the left and right

tables match. For example, we can’t have an invoice that isn’t linked to a customer,

and the customer must be inserted into the database before we’re allowed to cre-

ate an invoice for that customer. Note that you don’t need a value on both sides of

the join: you may have a customer without an invoice, but you may not have an

invoice without a customer.

If we can prove that the data in our tables has referential integrity, which most

data in financial business systems has, then we can use a referential join. In this

case, if we’re not reading any data from the right table, then SAP HANA can quite

happily ignore the entire right table and the join itself, and it doesn’t have to do

any checking, which speeds up the whole process.

To understand referential joins in a bit more detail, let’s look at an example. Let’s

say you have a table on the left of the join containing invoices. The table on the

right contains the products you sold. These tables are joined with a referential join.

Several reports query these tables.

If the report asks for data from both tables, the referential join behaves like an

inner join. If fields from the right table are requested, an inner join is executed.

The interesting aspects of a referential join kick in when a report only requests

fields from the left table containing the invoice data. When no data from the right

table is requested, the join isn’t executed. This is how SAP HANA optimizes the per-

formance of the queries. This case is very common in cubes, which are used in

most analytics, reporting, and business intelligence scenarios. (We’ll explain cubes

in the next sections.)

The only exception to this is the rare instance where the cardinality is 1:n. In this

case, the referential join has to execute the query, even if no fields from the right

table are requested. In that case, it executes an inner join as usual.

Inner joins are slower than referential joins as they will always execute the join,

even if no fields from the right table are requested. Left outer joins perform simi-

larly to referential joins, but they give different results.


Referential joins depend on referential integrity. Even the name of the join is

derived from this. If your application doesn’t ensure the referential integrity of

your data, you shouldn’t use this type of join. In such a case, you might get differ-

ent results, depending on whether the database query requests fields from the

right table.

Tip

A referential join is the optimal join type in SAP HANA.

Text Join

Another SAP HANA-specific joint type is a text join. The name gives a clue as to

when you’ll use this type of join. The right-hand table, as shown in Figure 5.5, is

something like a text lookup table—for example, a list of country names. On one

side, a Country code appears, such as US or DE. In the text lookup table, the same

Country code appears that will link with the key and also contain the actual name

of the country (e.g., United States or Germany).

Figure 5.5 Text Join

Text Join

Left Table

Right Table

Field 1 Field n Country code

DE

US

ZA

Field 1 Country code Country nameField nLanguage

code

DE EN Germany

Deutschland

Alemania

United States

South Africa

DE DE

DE ES

US EN

ZA EN


Note

SAP sells software in many countries, and SAP business systems are available in multiple

languages.

In Figure 5.5, the names of the countries are translated into different languages.

The fourth column in the text lookup table, called the Language code, indicates

into which language the country name has been translated. For example, for the

country called DE and a language code of EN, the name of the country is in English

and thus is Germany. If the language code was DE (for German), the name of the

country would be Deutschland, and if the language code was ES (for Español, indi-

cating Spanish), then the name of the country would be Alemania. Therefore, one

country can have totally different names depending on what language you speak.

In such a case, SAP HANA does something clever: By looking at the browser, the

application server, or the machine that you’re working on, it determines the lan-

guage in which you’ve logged in. If you’re logged in using English, it knows to use

the name Germany. If you’re logged in using German, it provides the German

country name Deutschland for you.

The text join behaves like a left outer join but always has a cardinality of one-to-

one (1:1). In other words, it only returns a single country name based on the lan-

guage you’re logged in with. Even if you’re logged in via mobile phone, your

mobile phone has a certain language setting.

Temporal Join

The temporal join is used when we have FROM and TO date and time fields, or inte-

gers. For example, say you’re storing the fuel price for gasoline in a specific table.

From the beginning of this month to the end of this month, gasoline sells for a cer-

tain price. Next month, the price differs, and, maybe two weeks later, it’s adjusted

again. Later, you’ll have a list of all the gasoline prices for different time periods.

As a car owner, you now want to perform calculations for how much you’ve paid

for your gasoline each time you filled up your tank. However, you just have the

number of gallons and the dates of when you filled up.

You can say, “OK, on this date, I filled up the tank.” You then have to look up which

date range your filling-up date falls within and find the price for that specific date.

You can then calculate what you paid to fill your tank for each of your dates. Figure

5.6 illustrates this example.


This date range lookup can be a little more complicated in a database. Sometimes,

programmers read the data into an application server and then loop through the

different records. In this case, a temporal join makes it easy because SAP HANA will

perform the date lookups in the FROM and the TO fields automatically for you, com-

pare it to the date you’ve supplied, and get you the right fuel price at that specific

date. It will perform all the calculations automatically for you—a great time and

effort saver.

Figure 5.6 Temporal Join for Gasoline Prices

A temporal join uses an inner join to do the work. A temporal join requires valid

FROM and TO dates (in the gasoline price table) and a valid date-time column (in your

car logbook table).

Spatial Join

Spatial joins became available in SAP HANA 1.0 SPS 09. SAP HANA provides spatial

data types, which we can use, for example, with maps. These all have the prefix ST_.

A location on a map, with longitude and latitude, is an ST_POINT data type. (We’ll

discuss spatial data and analytics further in Chapter 11.)

Temporal JoinLeft Table

Right Table—Pricelist

Field 1 Field n Date

27 February

14 April

13 May

Field 1 From To Gas Price

1 January 28 February 12.22

1 March 1 April 11.45

2 April 25 April 11.50

26 April 10 May 12.77

11 May 25 May 11.33


We can use special spatial joins between tables in SAP HANA (see Figure 5.7). Say

you have a map location stored in a ST_POINT data type in one table. In the other

table, you have a list of suburbs described in a ST_POLYGON data type. You can now

join these two tables with a spatial join and define the spatial join to use the ST_

CONTAINS method. For each of the locations (ST_POINT), this will calculate in which

suburb (ST_POLYGON) they are located (ST_CONTAINS).

Figure 5.7 Spatial Join

About a dozen methods like ST_CONTAINS are available for spatial joins. For exam-

ple, you can test if a road crosses a river, if one area overlaps another (e.g., mobile

phone tower reception areas), or how close certain objects are to each other.

Dynamic Joins

A dynamic join isn’t really a join type; it’s a join property. It’s also a performance

enhancement available for SAP HANA. After you’ve defined a join, you can mark it

as a dynamic join.

Note

For a dynamic join to work, you have to join the left and right tables on multiple columns.

Spatial Join usingmethod ST_CONTAINS

Left Table

Right Table

Field 1 Field n ST_POINT

(x1, y1)

(x2, y2)

(x2, y3)

Field 1 ST_POLYGON Field n


Let’s assume you define a static (normal) join on columns A, B, and C between two

tables, as shown in Figure 5.8. In this case, the join criteria on all three columns will

be evaluated every time the view is called in a query.

Figure 5.8 Dynamic Join on Multiple Columns

If you now mark the join as dynamic, the join criteria will only be evaluated on the

columns involved in the query to the view. If you only ask for column A in your

query of the view, then only column A’s join criteria will be evaluated. The join cri-

teria on columns B and C won’t be evaluated, and your system performance

improves.

Tip

If your query doesn’t request any of the join columns in the dynamic join, it will produce a

runtime error.

Lateral Join

We’ve discussed the join types that are used most often. You might also need to use

some specialized join types occasionally. One of these is a lateral join. The lateral

Dynamic Join

Left Table

Right Table

Field 1 A Field nB C

Field 1 A Field nB C


join helps you do row-by-row processing of data using the database engine, instead

of having to copy the data to the application server and processing it using loops.

Joins like these improve the performance of complex queries by ten or a hundred

times.

SPS 04

In SAP HANA SPS 04, we get a lateral join. Say you want to join a left table to a subquery.

Normally you would write the following:

SELECT A, B FROM TABLE_LEFT INNER JOIN (SELECT C, D FROM RIGHT_TABLE WHERE E < 100) ON …

However, you have cases where you want to let the subquery depend on values in the left

table. Instead of having E < 100 in the preceding statement, you want to use E < TABLE_LEFT.VALUE. You basically want a FOR EACH loop through the left table, computing which

rows should be joined from the right table. You can use a lateral join for this. The lateral

join works together with the INNER and LEFT OUTER joins.

We change the earlier line to the following:

SELECT A, B FROM TABLE_LEFT INNER JOIN LATERAL (SELECT C, D FROM RIGHT_TABLE WHERE E < TABLE_LEFT.VALUE) ON …

We’ve now discussed most of the join types. In the next section, we discuss a con-

cept that has become very important with SAP HANA modeling: CDS views.

Core Data Services Views

CDS is a modeling concept that has become important with SAP HANA 2.0 using

SAP HANA extended application services, advanced model (SAP HANA XSA), as

well as the new application programming model of SAP Cloud Platform. To under-

stand why we need CDS, it’s important to remember the wider context of how SAP

HANA systems are deployed.

While learning SAP HANA, you work with a single system, but productive environ-

ments normally include development, quality assurance, and production systems.

You perform development in the development environment, in which you have

special modeling and development privileges. Your models and code then enter

the quality assurance system for testing. When everyone is satisfied with the

results, the models and code move to the production system. You don’t have any

modeling and development privileges in the quality assurance and production

systems; you therefore need another way to create views and other database

objects in the production system, ideally without giving people special privileges.


An SAP HANA system can be deployed as the database for an SAP business system.

In such a case, ABAP developers in the SAP business system don’t have any model-

ing and development privileges, even in the SAP HANA database development sys-

tem. We need a way to allow ABAP developers to create views and other database

objects in the database system without these special privileges.

With that in mind, let’s look at why CDS is a good idea with the following example:

You need to create a new schema in your SAP HANA database. It’s easy to use a

short SQL statement to do this, but remember the wider context. Someone will

need the same create privileges in the production system! We want to avoid this

because giving anyone too much authorization in a production system can

weaken security and encourage hackers.

CDS allows for a better way to create such a schema: specify the schema creation

statement once by creating a CDS (text) file with the schema_name="DEMO"; instruc-

tion. CDS files have a .hdbcds file extension. When we deploy this CDS file to the

production system, the SAP HANA system automatically creates the schema for

us. In the background, SAP HANA automatically converts the CDS file into the

equivalent schema creation SQL statement and executes this statement.

The system administrators don’t get privileges to create any schemas in the pro-

duction system. They have rights to import CDS files, but they have no control

over the contents of the CDS files, for example, the name of the schema; that’s

decided by the business. CDS thus gives us a nice way to separate what the busi-

ness needs from what database administration can do.

There’s much more to CDS. You can specify table structures (metadata) and associ-

ations (relationships) between tables with CDS. When deploying this CDS file to

production, it creates the new tables with their relationships. Even more impres-

sive, if you want to modify the table structures later, you just update the CDS file.

When you redeploy the CDS file, SAP HANA doesn’t delete the existing tables and

re-create them; it automatically generates and executes the correct SQL state-

ments to modify only the minimal changes to the existing table structures, for

example, adding only a new column. CDS doesn’t replace SQL statements because

it ultimately translates back into SQL statements. CDS adds the awareness to use a

table creation SQL statement the first time it’s run, but a table modification SQL

statement the second time it’s run. The biggest advantage of this smart behavior is

that the data already stored in a table is kept. If we ran a table creation statement

the second time, it would delete the contents of the table. Not something we want

in a productive system!


Note

We don’t define the schema name when we create CDS artifacts such as tables. The

schema name is added automatically and is the name of the SAP HANA Deployment

Infrastructure (HDI) container into which the artifacts are deployed.

We can also define new semantic types for when we create tables. Instead of using

a DATE data type for a field, we can define a DELIVERY_DATE. On a lower level, it still

uses a DATE type, but it gives us some business context (e.g., deliveries) when defin-

ing the field. If we later change the semantic type, for example, we add more deci-

mal figures to a PAYMENT type, all tables automatically get updated to reflect the new

behavior.

We can also create views using CDS. These CDS views can be used as data sources

in our SAP HANA information views. Note that these CDS views don’t replace the

more powerful calculation views we can create in SAP HANA. (We discuss these in

Chapter 6 and Chapter 7.) Calculation views are very good for analysis and report-

ing. Developers who create OData services and SAP Fiori screens sometimes prefer

to stick to CDS views, using a text environment rather than the graphical tools.

ABAP developers can also use CDS inside ABAP. When a CDS view is sent to the SAP

HANA system, it creates the necessary SAP HANA database objects without the

ABAP developer having to log in to the SAP HANA database. Because ABAP is data-

base independent, the same CDS file sent to another database will create the data-

base objects relevant to that database. Because of this database independence, the

versions of CDS for ABAP and CDS for SAP HANA have slight differences.

In SAP HANA 1.0, we used the SAP HANA tools to create tables. In SAP HANA 2.0

with HDI, we use CDS to create tables. The tables in a database are often referred to

as the persistence layer.

Tip

If you want to learn CDS better, or are interested in learning more of SAP HANA XSA

development, we recommend that you look at the series of 64 videos that Thomas Jung

created on his private YouTube channel at http://s-prs.co/487601.

The example screens in this book make use of the SAP HANA Interactive Education

(SHINE) demo package described in Chapter 2. SHINE is a good example of CDS in

action. You don’t have to create a schema, create tables, or import data into these

tables. When SHINE is imported and deployed into your SAP HANA system, it

automatically creates all that content for you by using CDS statements. This shows

http://s-prs.co/487601


how using CDS in your SAP HANA projects keeps everything together—from the

creation of the persistence layer, to views, to security. When you deploy a project

to other systems, these artifacts are automatically created for you during the build

process.

Tip

The SAP Cloud Application Programming Model relies on CDS and is important for model-

ers and developers going forward.

The application programming model is a framework comprised of CDS, service Software

Development Kits (SDKs) for working with services in Java and Node.js, cloud-native plat-

form, and tools in SAP Web IDE working together with local tools on the same projects.

You can read more about the application programming model at http://s-prs.co/v487602.

On the page that appears, click on the Next> link to get a list of tutorials on this topic.

SPS 04

SAP Cloud Application Programming Model is supported with SAP HANA 2.0 SPS 04.

Now, let’s take our knowledge of these concepts to the next “dimension.”

Cube

One of the most important modeling concepts we’ll cover is the cube. We’ll expand

on this concept when we discuss the information views available in SAP HANA. In

this section, we’ll use a very simplistic approach to describing cubes to give you an

understanding of the important concepts.

When you look at a cube the first time, it looks like multiple tables that are joined

together, which might be true at a database level. What makes a cube a cube, how-

ever, are the rules for joining these tables together.

The tables you join together contain two types of data: transactional data and mas-

ter data. As shown in Figure 5.9, the main table in the middle is called a fact table,

which is where our transactional data is stored. The transactional data stored in

the fact table is referred to as either key figures or measures. In SAP HANA, we just

use the term measures.

The side tables are called dimension tables, and this is where our master data is

stored, which is referred to as characteristics, attributes, or sometimes even facets.



In SAP HANA, we just use the term attributes. In the following sections, we’ll walk

through an example and then provide you with some definitions of key terms.

Figure 5.9 Cube Features

Example

Our cube example will be simplified to communicate the most basic principles. In

our example, we’ll work with the sentence “Laura buys 10 apples.” You’ll agree that

this sentence describes a financial transaction: Laura went to the shop to buy

some apples, she paid for them, and the shop owner made some profit.

Where will we store this transaction’s data in the cube? There are a couple of rules

that we have to follow. The simplest rule is that you store the data on which you

can perform calculations in the fact table, and you store all other data on which

you can’t perform calculations in the dimension tables.

In our transaction of “Laura buys 10 apples,” we first look at Laura. Can we perform

a calculation on Laura? No, we can’t. Therefore, we should put her name into one

of the dimension tables. We put her name in the top dimension table in Figure

Dimension TableFact Table

Dimension Table

Dimension Table

ContainsAttributes

ContainsAttributes

ContainsAttributes

ContainsMeasures


5.10. Laura isn’t the only customer of that shop; her name is stored with those of

many other people in this dimension table. We’ll call this table the customer

dimension table.

Figure 5.10 Financial Transaction Data Stored in a Cube

Next, let’s look at the apples. Can we perform calculations on apples? No, we can’t,

so again this data goes into a dimension table. The shop doesn’t sell only apples; it

also sells a lot of other products. The second dimension table is therefore called the

product dimension table.

In an enterprise data warehouse (EDW), we would normally limit the number of

dimension tables that we link to the fact table for performance reasons. Typically,

in an SAP Business Warehouse (SAP BW) system, we limit the number of dimen-

sion tables to a maximum of 16 or 18.

Finally, let’s look at the number of apples, 10. Can we perform a calculation on

that? Yes we can, because it’s a number. We therefore store that number in the fact

table.

To complete the picture, if Laura buys more apples, then she is a top customer; let’s

say she’s customer number 2. Apples are something the store keeps in stock, so

apples are product number 7.

If we store a link (join) to customer 2 and another link (join) to product 7 and the

number 10 (number of apples), we would say that it constitutes a complete trans-

action. We can abbreviate the transaction “Laura buys 10 apples” to 2, 10, and 7 in

Product 7—Apples

Customer 2—Laura,Product 7—Apples,

Quantity—10

Customer 2—Laura

Customer table

Product table

Financial transaction:Laura buys 10 apples


our specific cube (2 indicates Laura, 10 indicates the number of apples, and 7 the

apples).

Laura and apples are part of our master data. The transaction itself is stored in the

fact table. We’ve now put a real-life transaction into the cube.

Tip

You can have master data without associated transactions. As the shopkeeper, you have

apples (master data) for sale, but no one has bought them yet (no transaction). Similarly

you can have customers such as Laura (master data) but no invoice yet.

The reverse doesn’t apply, however. You can’t have an invoice without a customer or

products.

Hopefully, this gives you a better idea of what a cube is and how to use it. Let’s

complete this short tour of cubes by clarifying a few terms ahead.

Key Terms

As previously shown in Figure 5.9, attributes are stored in dimension tables, and

measures are stored in fact tables. Let’s define attributes and measures:

� Attributes

Attributes describe the parts of a transaction, such as the customer Laura or the

product apples. This is also referred to as master data. We can’t perform calcula-

tions on attributes.

� Measures

Measures are something we can measure and perform calculations on. Measur-

able (transactional) information goes into the fact table.

Other key terms to be aware of are as follows:

� Fact tables in the data foundation

Figure 5.10 showed only a single fact table, but sometimes we have more com-

plex cubes that include multiple fact tables. In SAP HANA, we call a group of fact

tables (or the single fact table) the data foundation, although some people refer

to the entire group of tables as the fact table.

Tip

When we join the data foundation (fact tables) to the dimension tables, we always put

the data foundation on the left side of the join.


� Star joins

The join between the data foundation and the dimension tables is normally a

referential join in SAP HANA. We refer to this as a star join, which indicates that

this isn’t just a normal referential join between low-level tables but also a join

between a data foundation and dimension tables. This is seen as a join at a

higher level.

Remember

You use star joins to join dimension tables to a fact table.

With all the building blocks in place, let’s start examining the graphical SAP HANA

information views.

Calculation Views

Let’s start our introduction to SAP HANA information views by looking back at

what we learned when we discussed cubes. When we graphically build complex

views in SAP, we call these calculation views. These are different from SQL views

and CDS views. Calculations views can be far more powerful and complex than

these other views.

We’ll now take what we’ve learned, and see how SAP HANA implements this with

different types of calculation views. We can then combine these calculation views

to build complex information models.

Types of Calculation Views

SAP HANA has three types of calculation views:

� Dimension views

The first thing we need to build cubes is the master data. Master data is stored in

dimension tables. Sometimes, dimension tables are created for the cubes. In

SAP HANA, we don’t create dimension tables as separate tables; instead, we

build them as views by using the master data tables.

We join all the low-level database tables that contain the specific master data

we’re interested in into a view. For example, we might join two tables in a view

to build a new Products dimension in SAP HANA, and maybe another three

tables to build a new Customers dimension.


We use calculation views of type dimension, also called dimension calculation

views. In this book, we’ll refer to these type of views simply as dimension views.

Just as the same master data is used in many cubes, we’ll reuse dimension views

in many of the other SAP HANA information views. We can even build our own

library of dimension views.

� Star join views

In the same way, instead of storing data in cubes, we can create another type of

view in SAP HANA that produces the same results as a traditional cube. These

views produce the same results as an old-fashioned, traditional cube called cal-

culation views of type cube with star join.

Terminology

In this book, we’ll simply refer to these types of views as star join views, not as cube views,

which would lead to confusion. This is because the third type of SAP HANA view is a calcu-

lation view of type cube.

The results created by the star join views in SAP HANA are the same as that of a

cube in an EDW, except that in a data warehouse, the data is stored in a cube.

With SAP HANA, the data isn’t stored in a cube but is calculated when required.

To build a star join view, follow these steps:

– Create the dimension views you need as separate calculation views of type

dimension.

– In the calculation view of type cube with star join (star join view), join the

transaction tables together to form the data foundation.

– Join the data foundation (or single fact table) to the dimension calculation

views using star joins.

� Cube views

This view type provides even more powerful capabilities. For example, you can

combine multiple star join views (“cubes”) to produce a combined result set. In

SAP BW, we call this combined result set a MultiProvider. In SAP HANA, we call

these calculation views of type cube. Use cases for this type of view include the

following:

– In your holding company, you want to combine the financial data from mul-

tiple subsidiary companies.

– You want to compare your actual expenses for 2019 with your planned bud-

get for 2019.


– You have archived data in an archive database that you want to combine with

current data in SAP HANA’s memory to compare sales periods over a number

of years.

Using and Building the Information Model

Now that you know how to build individual views, let’s examine how to use the

different types of SAP HANA calculation views together in information modeling.

Figure 5.11 provides an overview of everything you’ve learned in this chapter and

how each topic fits together.

Figure 5.11 Different SAP HANA Calculation Views Working Together

On the top left-hand side, you can see our source systems: an SAP source system

and a non-SAP source system. SAP HANA doesn’t care from which system the data

comes. In many projects, we have more non-SAP systems that we get data from

Data provisioning tool, e.g. SDI

SAP source system

Database

Non-SAP source system

Database

Report or query

HTML5 browser

Star-join view

(“Cube”)

Star-join view

(“Cube”)

Application server

SAP HANA

Calculation view of type cube

Union

Star-join view (“Cube”)Tables

Table 1 Table 8

Table 2 Table 9

Table 3 Table 10

Table 4 Table 11

Table 5 Table 12

Table 6

Table 7 Data Foundation

Apples

Quantity – 10

Laura

Customerdimension view

Productsdimension view

Star Join(Logical Join)


than SAP systems. We extract the data from these systems using various data pro-

visioning tools, which we’ll look at in Chapter 9.

The data is stored in row tables in these source systems. When we use data provi-

sioning methods and tools, the data is put into column tables in the SAP HANA

database, as illustrated in the bottom-left corner of Figure 5.11.

Now, let’s start building our information models on top of these tables. If we want

to build something like a cube for our analytics or reporting, we use SAP HANA cal-

culation views. First, we need the equivalent of a dimension table, such as our

product or customer dimension table. To do this, we’ll build a dimension view (a

calculation view of type dimension). This is indicated in Figure 5.11 by Laura and

Apples, which represent the customer and product dimension views. We create

these dimension views the same way we create any other view: take two tables,

join them together, select certain output fields, and save it.

After the dimension views are created, we build a data foundation for the cube. The

data foundation is for all the transactional data. The data foundation stores mea-

sures, such as the number 10 for the 10 apples that Laura bought. We build a data

foundation in a similar fashion to building a dimension view via either multiple

tables or a single table. If using multiple tables, we join them together, select the

output fields, and build our data foundation.

To complete the cube, we have to link our data foundation (fact tables) to the

dimensions with a star join. Finally, to complete our star join view, we also select

the output fields for our cube. The created star join view will produce the same

result as a cube, but, in this case, no data is stored, and it performs much better.

In the next step, we can create a calculation view of type cube, in which we com-

bine two star join views (cubes). We can combine the star join views using a join or

a union. We recommend a union in this case because it leads to better perfor-

mance than a join.

Finally, we can build reports, analytics, or applications on our calculation view, as

shown in the top right of Figure 5.11.

Let’s now think about the process from the other side and look at what happens

when we call our report or application that is built on the calculation view on the

right side of our diagram. This is the top-down view of Figure 5.11.

The calculation view doesn’t store the data; instead, it has to gather and calculate

all the data required by the report. SAP HANA looks at the calculation view’s

description and recognizes that it has to build two star join views and union them

together by first determining how to build the star join views.


SAP HANA then goes down one level and determines that to build the first star join

view, it needs a data foundation and two dimension views, and it then joins them

together with a star join.

Then, it goes down another level and determines that to build the dimension view,

it must read two different tables, join them together, and select the output fields.

SAP HANA then builds the two dimension views.

Going back up one level, SAP HANA needs to combine these two dimension tables

with a data foundation. It creates the data foundation and joins it with the two

dimension tables using a star join. It also builds the second star join view in the

same way. Going up to the level of the calculation view, it combines the two star

join views with a union. Finally, SAP HANA sends the result set out to the report

and then cleans out the memory again. (It doesn’t store this data!) If someone else

asks for the exact same data five minutes later, SAP HANA happily performs the

same calculations all over again.

At first, this can seem very wasteful. Often we’re asked “Why don’t you use some

kind of caching?” However, caching means that we store data in memory. In SAP

HANA, the entire database is already in memory. We don’t need caching because

everything is in memory already.

The reason people ask this question is that in their traditional data warehouses,

data normally doesn’t change until the next evening when the data is refreshed.

Inside SAP HANA, the data can be updated continuously, and we always want to

use the latest data in our applications, reports, and analytics. That means we have

to recalculate the values each time; caching static result sets won’t work.

In summary, to use real-time data, you have to recalculate as data gets updated.

Other Modeling Artifacts

We’ll now discuss some of the other modeling concepts we’ll use when building

our information models.

The power of calculation views of type cube is that you can build them up in many

layers. In the bottom layer, you can perhaps have a union of two star join views

(cubes), as shown in Figure 5.12.

In the next (higher) layer, we sum up (aggregate) all the values from the union. In

the level above that, we can rank the summed results to show the top 10 most prof-

itable customers.

Some of the artifacts we can use are discussed in the following subsections.


Figure 5.12 Calculation View of Type Cube Built in Various Layers

Projection

A projection is used to change an output result set. For example, we can get a result

set from a star join view (cube) and find that it has too many output fields for what

we need in the next layers. Therefore, we use a projection as an additional in-

between layer to create a subset of the results.

We can also use the projection to calculate additional fields—for example, sales

tax—or we can rename fields to give them more meaningful names for our analyt-

ics and reporting users.

Ranking

As illustrated in the top node of Figure 5.12, we can use ranking to create useful top-

N or bottom-N reports and analytics. This normally returns a filtered list (e.g., only

the top 10 companies) and is sorted in the order we specify.

Union

Aggregation (e.g., Sum)

Ranking (e.g., Top 10)

Star join view Star join view

Customers

Customer 2

Customer 1

Total Sales

$500

$400

Customers

Customer 1

Customer 2

Total Sales

$400

$500

Customers

Customer 1

Customer 2

Sales for 2015

$100

$150

Customers

Customer 1

Customer 2

Sales for 2016

$300

$350

Customers

Customer 1

Customer 1

Total Sales

$100

$300

Customer 2

Customer 2

$150

$250

Total Sales

2018

2019

2018

2019


Aggregation

The word aggregation means a collection of things. In SQL, we use aggregations to

calculate values such as the sum of a list of values. Aggregations in databases pro-

vide the following functions:

� Sum

Adds up all the values in the list.

� Count

Counts how many values (records) there are in the list.

� Minimum

Finds the smallest value in the list.

� Maximum

Finds the largest value in the list.

� Average

Calculates the arithmetical mean of all the values in the list.

� Standard deviation

Calculates the standard deviation of all the values in the list.

� Variance

Calculates the variance of all the values in the list.

Normally, we would only calculate aggregations of measures. (Remember that

measures are values we can perform calculations on, which usually means our

transactional data.) For example, we could calculate the total number of apples a

shop sold in 2019. However, in SAP HANA, we can also create aggregations of attri-

butes, essentially creating a list of unique values.

Union

A union combines two result sets. Figure 5.12, shown previously, illustrates this by

combining the results of two star join views.

In SAP HANA graphical calculation views, we’re not restricted to combining two

result sets; we can combine multiple result sets. SAP HANA merges these multiple

result sets into a single result set.

Let’s say we have four result sets—A, B, C, and D—that we union together.

Although we union multiple result sets, that doesn’t mean the performance will be

bad. If SAP HANA detects that you’re not asking for data from A and D, it won’t

even generate the result sets for A and D; it will only work with B and C.


Note

The union expects the two result sets to have the same number of columns in the same

order, and the matching columns from each result set must have similar data types.

You can have variations in unions, such as the following:

� Union all

Merges the result sets and returns all records.

� Union

Merges the results and only returns the unique results.

� Union with constant values

Lets you “pivot” your merged results to help get the data ready for reporting

output.

Figure 5.13 compares the output generated by a union to that of a union with con-

stant values.

Figure 5.13 Standard Union Compared to Union with Constant Values

Often, we want a report that looks like the top right of the figure. If we’re given the

output from the standard union (top left), it takes more work to create a report

with sales for 2018 and 2019 in separate columns. With the output from the union

with constant values, writing the report is easy.

Standard union Union with constant values

Customers

Customer 1

Customer 1

Total Sales

$100

$300

Customer 2

Customer 2

$150

$250

Total Sales

2018

2019

2018

2019

Customers

Customer 1

Customer 2

Sales for 2015

$100

$150

Customers

Customer 1

Customer 2

Sales for 2016

$300

$350

Customers

Customer 1

Customer 2

Sales for 2015

$100

$150

Sales for 2016

$300

$350


Earlier in Figure 5.11, we showed that you should use a union rather than a join for

performance reasons. But what happens if the two star join views that you want to

union together don’t have the same output fields? In that case, the union will

return an error. How do we solve this problem?

Figure 5.14 illustrates an example. On top, we have the ideal case in which both star

join views have matching columns. In the middle, we have a case in which only col-

umn B matches. Normally, we would use a join, as shown.

We solve this problem by creating a projection for each of the two star join views.

We add the missing columns (field C in the left table, and field A in the right table)

and assign a NULL value to those new additional columns. We can then perform a

union on the two projections because they now have matching columns.

Figure 5.14 Standard Union, Join, and Join Converted to Union

In SAP HANA, we can perform two more operations on the result sets of the left

and right tables. Instead of just combining the two result sets in a union, we can

Union

Join convertedto union

Right Table

B

C

Left Table

A

B

Right Table

A

B

Left Table

A

CC

B

Right Table

A = NULL

B

Left Table

A

CC = NULL

B

Inner Join

B (left side) B (right side)

Inner Join

A (left side)

A (right side)

B (left side)

C (left side)

B (right side)

Union

A (left side)

B (left side)

B (right side)

C (right side)

C (right side)


find out which records occur in both the result sets or, alternatively, which records

occur only in one of the two result sets.

Imagine you have a marketing campaign where you want to sell gaming head-

phones to customers. In this case, you might want to target only those customers

who bought both a computer and a large wide-screen monitor. You’ll have one

result set with customers who bought computers, and another result set with cus-

tomers who bought large wide-screen monitors. Instead of using a union, you’ll

now use an intersect operation for use in your marketing campaign.

However, if your marketing campaign is to sell large wide-screen monitors to cus-

tomers who bought computers but did not buy any screens, you would use a

minus operation.

We’ll look at these again in the next chapter.

Semantics

An important step that some modelers skip is to add more meaning to the output

of their information models. Semantics, as a general term, is the study of meaning.

For example, we can use semantics in SAP HANA to rename fields in a system so

their purpose becomes clearer. Some database fields have really horrible names.

Using semantics, you can change the name of these fields to be more meaningful

for your end users. This is especially useful when users create reports. If you have

a reporting background, you’ll know that a reporting universe can fulfill a similar

function.

We can also show that certain fields are related to each other, for example, a cus-

tomer number and a customer name field. Another example that is used often is

that of building a hierarchy. By using a hierarchy, you can see how fields are

related to each other with regards to drilling down for reports.

Hierarchies

Hierarchies are used in reporting to enable intelligent drilldown. Figure 5.15 illus-

trates two examples: users can start with a list of sales for all the different coun-

tries; they can then drill down to the sales of the provinces (or states) of a specific

country and finally down to the sales of a city.

End users can see the sales figures for just the country, region (state or province),

or city (town). Even though countries and cities are two different fields, they’re


related to each other by use of a hierarchy. This extra relationship enhances the

data and gives it more meaning (semantic value).

Figure 5.15 Examples of Level Hierarchies

We’ll take a look at two different types of hierarchies: level hierarchies and parent-

child hierarchies. The hierarchy we just described is the level hierarchy; level 1 is the

country, level 2 is the state or province, and level 3 is the city or town.

Another example of a level hierarchy is a time-based hierarchy that goes from a

year to a month to a day. You can even keep going deeper, down to seconds. This is

illustrated on the right side of Figure 5.15.

HR employee organizational charts or cost center structures are examples of par-

ent-child hierarchies, as illustrated in Figure 5.16. In this case, we join a table to

itself, as we did when describing self-joins.

Location level hierarchy Date level hierarchy

South Africa

Gauteng

Johannesburg

Pretoria

KwaZulu-Natal

Durban

Western Province

Cape Town

2019

January

1

2

February

28

May

31

Location level hierarchy

Country

Region (Province, State)

City, Town

Date level hierarchy

Year

Month

Day


Figure 5.16 Parent-Child Hierarchy Example

Table 5.1 lists the hierarchies we discussed and when you should use which one.

Level Hierarchies Parent-Child Hierarchies

Fixed (rigid) number of levels are used in the hierarchy. Variable hierarchy depth is used.

Different data fields are used on every level of the hier-

archy (e.g., country, state, and city).

The same (two) fields are used

on every level of the hierarchy.

The fields on each level of the hierarchy can be different

data types (e.g., country can be text, while ZIP/postal

code can be numeric).

The parent and child fields have

the same data type.

Table 5.1 Comparing Level Hierarchies and Parent-Child Hierarchies Best Practices and Modeling Guidelines

CEO (is also employee 1)

Manager 1 (is also employee 2)

Employee 3

Employee 4

Manager 2 (employee 5)

Employee 6

Employee 7

Employee 8

CEO

Manager 1 Manager 2 Employee 8

PersonalAssistant

Parent-child hierarchy

Manager-employee hierarchy

Employee ID

Employee name

Employee surname

…

…

Manager ID

…

…

Data for Employee 2

Employee 2

Manager 1

…

…

…

Employee 1 (the CEO)

…

…


Let’s end our tour of modeling concepts with a summary of best practices for mod-

eling views in SAP HANA. Many of the basic modeling guidelines that we find in

traditional data modeling and reporting environments are still applicable in SAP

HANA. We can summarize the basic principles as follows:

� Limit (filter) the data as early as possible.

It’s wasteful to send millions of data records across a slow network to a report-

ing tool when the report only uses a few of these data records. It makes a lot

more sense to send only the data records that the report requires, so we want to

filter the data before it hits the network.

In the same way, we don’t want to send data to a star join view when we can fil-

ter it in the earlier dimension view—hence, the rule to filter as early as possible.

� Perform calculations as late as possible.

Assume you have column A and column B in a table. The table contains billions

of records. We can add the values of column A and column B in a formula for

every data record, which means the server performs billions of calculations and

finally adds up the sum total of all these calculated fields.

It would be much faster to calculate the sum total for column A and the sum

total for column B and then calculate the formula. In that case, we perform the

calculation once instead of billions of times. We delay the calculation as long as

possible for performance reasons.


In this chapter, we focused on various modeling concepts and how they’re used in

SAP HANA. We introduced the following important terms:

� Views

We started by looking at views and realized that we can leverage the fact that

SAP HANA already has all the data in memory, that the servers have lots of CPU

cores available, and that SAP HANA runs everything in parallel. We therefore

don’t need to store the information in cubes and dimension tables but can use

views to generate the information when we need it. This also gives us the advan-

tage of real-time results.

� Joins

We have both normal database join types, such as inner and left outer joins, and

SAP HANA-specific types, such as referential, text, temporal, and spatial joins. In

the process, we also looked at the special case of self-joins and at dynamic joins

as an optimization technique.


� Core data services (CDS)

CDS provides a way to separate business semantics and intent from database

operations.

� Cubes

A cube consists of a data foundation made up of fact tables that contain the

transactional data and that are linked to dimension tables that contain master

data. The data foundation is linked to the dimension tables with star joins.

� Attributes and measures

Attributes describe elements involved in a transaction. Transactional data that

we can perform calculations on is called a measure and is stored in the fact

tables.

� Calculation views

In SAP HANA, we take the concept of views to higher levels. We can create

dimension tables and cubes as dimension views and star join views. Dimension

views are calculation views of type dimension. Star join views are calculation

views of type cube with a star join. The final type of information view is the cal-

culation view of type cube, which we can use to perform even more powerful

processes.

� Modeling artifacts

Inside our views, we can use unions, projections, aggregations, and ranking.

Unions are quite flexible and can be used for a direct merge of result sets, or we

can use a union with constant values to create report-ready result sets.

Practice Questions






the “Practice Question Answers and Explanations” section. Remember, on the

exam, you must select all correct answers and only correct answers to receive


In this section, we have a few questions with graphics attached. In the certification

exam, you might also see a few questions that include graphics.


1. In which SAP HANA views will you find measures? (There are 2 correct

answers.)

� A. Calculation view of type dimension

� B. Calculation view of type cube with star join

� C. Calculation view of type cube

� D. Database views

2. True or False: A database view stores data in the database.

� A. True

� B. False

3. A traditional cube is represented by which SAP HANA view type?

� A. CDS view

� B. SQL view

� C. Calculation view of type cube with star join

� D. Calculation view of type dimension

4. A referential join gives the same results as which other join type?

� A. Inner join

� B. Left outer join

� C. Spatial join

� D. Star join

5. Which join type makes use of date ranges?

� A. Spatial join

� B. Text join

� C. Temporal join

� D. Inner join

6. If we change the transaction “Laura buys 10 apples” to “Laura buys 10 green

apples,” how would we store the color green?

� A. Store green as an attribute.

� B. Store green as a measure.


� C. Store green as a spatial data type.

� D. Store green as a CDS view.

7. True or False: A view always shows all the available columns of the underlying

tables.

� A. True

� B. False

8. You have a view with two tables joined by a left outer join. If you redesign the

view and accidently swap the two tables around, what should you do to the

join?

� A. Keep the left outer join.

� B. Change the join to a text join.

� C. Change the join to a right outer join.

� D. Change the join to a referential join.

9. What do you call the data displayed in the data foundation of an SAP HANA

information view?

� A. Facets

� B. Measures

� C. Characteristics

� D. Key figures

10. You’re writing a mobile application for the World Series. You have details

about all the baseball players and the baseball scores for all the previous

games. How do you use the data of the player information and the scores?

� A. Both the player information and the scores are used as master data.

� B. Both the player information and the scores are used as transactional data.

� C. The player information is used as master data, and the scores are used as

transactional data.

� D. The player information is used as transactional data, and the scores are

used as master data.


11. Look at Figure 5.17. You’re selling books that have been translated into various

languages. What join type should you use?

� A. Left outer join

� B. Text join

� C. Temporal join

� D. Referential join

Figure 5.17 What Type of Join Should You Use?

12. Which of the following are reasons you should use core data services (CDS) to

create a schema? (There are 2 correct answers.)

� A. The database administrator might type the name wrong.

� B. The database administrator should not get those privileges in the produc-

tion system.

� C. The focus is on the business requirements.

� D. The focus is on the database administration requirements.

Left Table

Right Table

SPRAS Title

EN

CN

AR

FR

RU

What join should you use here?

Field 1 Field n Book ID

Field 1 Field nBook ID


13. Look at Figure 5.18. A company sends out a lot of quotes. Some customers

accept the quotes, and they’re invoiced. The company asks you to find a list of

the customers that did NOT accept the quotes. How do you find the customers

that received quotes but did NOT receive invoices?

� A. Use a right outer join.

Filter to show only the NULL values on the right table.

� B. Use a left outer join.

Filter to show only customers on the right table.

� C. Use a left outer join.

Filter to show only the NULL values on the right table.

� D. Use a right outer join.

Filter to show only customers on the left table.

Figure 5.18 Find the Customers That Received Quotes but Not Invoices


Left Table—Quotes

Right Table—Invoices

Quote ID … Customer ID

Invoice ID … …Customer ID


14. True or False: When you use a parent-child hierarchy, the depth in your hier-

archy levels can vary.

� A. True

� B. False

15. Look at Figure 5.19. What type of join should you use? (Hint: Watch out for dis-

tractors.)

� A. Temporal join

� B. Text join

� C. Referential join

� D. Inner join

Figure 5.19 What Type of Join Should You Use?

16. You have a list of map locations for clinics. The government wants to build

a new clinic where there is the greatest need. You need to find the largest

From airport

Johannesburg

Frankfurt

JFK

Madrid

London


Right Table—Flights

Left Table—Persons

Languagecode

EN

DE

ES

EN

EN

Field 1

Amanda

Katrin

Sarah

Sarah

Amanda

To airport

Frankfurt

JFK

Madrid

Field 1

London

Cape Town

Field n Person

Sarah

Katrin

Amanda


distance between any two clinics. With your current knowledge, how do

you do this?

� A. Use a temporal join to find the distance between clinics.

Use a union with constant values to “pivot” the values.

� B. Use a spatial join to find the distance between clinics.

Use a level hierarchy for drilldown.

� C. Use a dynamic join to find the longest distance between clinics.

Use a ranking to find the top 10 values.

� D. Use a spatial join to find the distance between clinics.

Use an aggregation with the maximum option.

17. Look at Figure 5.20. What are the values for X, Y, and Z? (Note: You won’t see a

question like this on the certification exam; it’s only added here to test your

understanding.)

Figure 5.20 Find Values for X, Y, and Z

Union

Aggregation (Average)

Ranking for Bottom 10


Customers Average Sales

Customers

Customer 1

Customer 2

Sales for 2015

$100

$200

Customers

Customer 1

Customer 2

Sales for 2016

$300

$600

Customers

Customer 1

Customer 1

Sales

Customer 2

Customer 2

Total Sales

2018

2019

2018

2019

X

Y

Customers Average Sales

Z

Customer 1

Customer 2


� A. X = Customer 2, Y = 400, Z = 800

� B. X = Customer 1, Y = 400, Z = 400

� C. X = Customer 1, Y = 800, Z = 800

� D. X = Customer 2, Y = 200, Z = 400

18. You have to build a parent-child hierarchy. What type of join do you expect to

use?

� A. Relational join

� B. Temporal join

� C. Dynamic join

� D. Self-join

19. You have two result sets: one of customers who booked the venue for an

event, and another of customers who want flowers for the event. What do you

use to find the customers who booked both the venue and flowers?

� A. Union

� B. Minus

� C. Intersect

� D. Inner join

20. Look at Figure 5.21. What join type should you use to see all the suppliers?

� A. Left outer join

� B. Right outer join

� C. Inner join

� D. Referential join


Figure 5.21 What Join Should You Use to See All Suppliers?



You find measures (and perhaps attributes) in calculation views of type cube

with star join and calculation views of type cube. Calculation views of type

dimension don’t contain measures—only attributes.


False. A database view doesn’t store data in the database.


A traditional cube is represented by calculation view of type cube with star join.

It’s different from a cube in that they don’t store any data like a cube does. A cal-

culation view of type dimension is similar to a dimension table. CDS and SQL

views aren’t like cubes either.

What join should you use to see all the suppliers?

Supplier ID

1

2

4

5

7

Right Table—Suppliers

Left Table—Purchases

Field 1 … Supplier ID

1

5

3

Supplier name

Supplier A

Supplier B

Supplier D

Supplier E

Supplier G

… Column n



A referential join gives the same results as an inner join but speeds up the cal-

culations in some cases by assuming referential integrity of the data. It’s the

optimal join type in SAP HANA.


A temporal join requires FROM and TO fields in the table on the right side of the

join and a date-time column in the table on the left side of the join.


We’ll store the color green as an attribute. We can’t “measure” or perform cal-

culations on the color green. Green isn’t a spatial data type. CDS views aren’t rel-

evant in this context.


False. A view doesn’t always show all the available columns of the underlying

tables. You have to select the output fields of a view. The word always in the

statement is what makes it false.


A right outer join is the inverse of the left outer join. Therefore, if you reverse

the order of the tables, you can “reverse” the join type. It’s important to note

that this doesn’t work for a text join. A text join is only equivalent to a left outer

join. For a text join, you have to always be careful about the order of the tables.


The data displayed in the data foundation of an SAP HANA information view is

called measures. In SAP HANA, we talk about attributes and measures. The other

names might be used in other data modeling environments, but not in SAP

HANA.


The player information is used as master data, and the scores are used as trans-

actional data. You can’t perform calculations on the players. Therefore, this

data is used in the dimensions and is seen as master data. You definitely per-

form calculations on the scores, so these are used in the fact tables, which

means they are transactional data.


Figure 5.17 shows the SPRAS column and a list of languages. The need for a

translation is also hinted at in the question, meaning this should be a text join.

There isn’t enough information to select any of the other answer options.



You should use CDS to create a schema because the database administrator

should not get those privileges in the production system, and the focus should

be on the business requirements.


Look carefully at Figure 5.18. The question asks you to find all the customers

who received quotes but did not receive invoices.

The answer only gives us left outer or right outer as options. Because the ques-

tion states they want all customers from the quotes (left) table, you might try a

left outer join first.

Now look at Figure 5.3 to see the difference between the left and right outer

joins. Look especially at where the NULL values are shown. With a left outer join,

the NULL values are found in the right table. With a right outer join, the NULL val-

ues are found in the left table.

We want to find the customers who did not get invoices. With a left outer join,

the NULL values are found on the right side: that group is the one we’re looking

for. Therefore, filter for the NULL values on the right table.

Note

Any time a negative phrase or word is used in the certification exam, it’s shown in capital

letters to bring attention to the fact that it’s a negative word.


True. Yes, the depth of your hierarchy levels can vary when you use a parent-

child hierarchy.


You should use a referential join.

There are two distractors to try and throw you off track:

A temporal join uses FROM and TO fields but only for date-time fields. This exam-

ple is for airports.

The second distractor was to try to get you to select the text join. The language

code has nothing to do with translations but has to do with the airline’s pri-

mary language.

Choose a referential join because you can see the same people traveling in both

tables, so it seems that these two tables have referential integrity.


If the tables didn’t have referential integrity, you would have chosen an inner

join.


Only a spatial join can give the distance between different points on a map.

After you have the distances, there are two ways you can find the largest dis-

tance between any two clinics:

Use a ranking to find the top 10 values.

Use an aggregation with the maximum option.


See the full solution in Figure 5.22.

X = Customer 1, Y = 400, Z = 400.

Note that we’re using average in the aggregation node, not sum.

In the ranking node, we’re asking for the bottom 10, not the top 10.

Figure 5.22 Full Solution: X = Customer 1, Y = 400, Z = 400

Union

Aggregation (Average)

Ranking for Bottom 10


Customers

Customer 1

Customer 2

Total Sales

200

400

Customers

Customer 1

Customer 2

Total Sales

200

400

Customers

Customer 1

Customer 2

Sales for 2015

$100

$200

Customers

Customer 1

Customer 2

Sales for 2016

$300

$600

Customers

Customer 1

Customer 1

Total Sales

100

300

Customer 2

Customer 2

200

600

Year

2018

2019

2018

2019



You expect to use a self-join when you build a parent-child hierarchy. Refer to

Figure 5.16 for an illustration.


You use an intersect operation to find the customers that booked both the

venue and flowers.


To see everything on the right table, use a right outer join.

Note that these tables don’t have referential integrity. If we had asked the ques-

tion a little differently, this could have been important.

Takeaway

You should now have a general understanding of information modeling concepts:

views, joins, cubes, star joins, data foundations, dimension tables, and so on. In

this chapter, you learned about the differences between attributes and measures

and the value of CDS in the development cycle. You’ve seen the various types of

views that SAP HANA uses, how to use them together, and what options you have

available for your information modeling. Finally, we examined a few best practices

and guidelines for modeling in SAP HANA in general.

Summary

You’ve learned about many modeling concepts and how they are applied and

implemented in SAP HANA.

You’re now ready to go into the practical details of learning how to use the various

SAP HANA calculation views and how to enhance these views with the various

modeling artifacts that we have available to us in each of these different views.

Chapter 6

Calculation Views


� Learn to create SAP HANA calculation views

� Identify which type of SAP HANA calculation view to use

� Explore the various types of calculation views

� Know how to select the correct joins and nodes

� Get to know the value of semantics in information modeling

Chapter 6 Calculation Views232

In the past two chapters, we discussed the modeling concepts and tools required

for building SAP HANA information views. In this chapter and the next, we’ll build

on that foundation. We’ll focus on how to graphically create information views in

the SAP HANA system and how to enhance these views with additional functional-

ity.

In this chapter, you’ll learn to create the different types of calculation views, select

the correct nodes for what you want to achieve, and use the Semantics node to add

additional value for business users.

Real-World Scenario

You start a new project in which a business wants to expand the range of ser-

vices it offers its customers. You’re responsible for building the information

models in SAP HANA.

You have to build about 50 new SAP HANA information views. As you plan

your information views, you notice several master data tables that you’ll use.

By creating a few well-designed dimension views, you cater not only to the

needs of this project but also to future requirements. You see this as a

“library” of reusable dimension views.

While you’re busy designing these dimension views, you also gather some

analytics and reporting requirements. Most of the analytics requirements

are addressed by building star join views and calculation views of type cube.

You address some of the reporting requirements by delivering semantically

enhanced information models that are easier for your colleagues and end

users to work with.

The company wants to create a mobile app. Working together with some of

the developers, you quickly create a few calculation views to provide them

with information that would have taken them hours to create via code. The

performance of the calculation views you created are also very good, which

delights the mobile app users.


The purpose of this portion of the SAP HANA certification exam is to test your

knowledge of modeling SAP HANA information views.



topics:

� Creating SAP HANA information views

� Calculation views of type dimension

� Calculation views of type cube with star join

� Calculation views of type cube

� Selecting the correct joins and nodes to produce accurate results

� Choosing the correct type of SAP HANA information view

� Understanding the value that semantics adds to the information-modeling

process

Note

This portion contributes more than 10% of the total certification exam score.


In Chapter 4, we examined SAP HANA modeling tools, and, in Chapter 5, we looked

at modeling concepts. Now, it’s time to put them together, implementing the

modeling concepts with the tools to create SAP HANA calculation views.

There are three types of SAP HANA calculation views, which we briefly covered in

Chapter 5. First are dimension views, for the master data in the system. All data in

these views are treated as attributes, even numeric fields. Second, star join views

let you build cube-like information models. Third, calculation views of type cube

let you build enhanced multidimensional data structures and information mod-

els.

Tip

We refer to calculation views of type dimension as dimension views and to calculation

views of type cube with star join as star join views. The last category is called calculation

views of type cube.

The last category can cause some confusion because some people and older web pages

still refer to the last category simply as calculation views. In this book, we use the latest

official name—calculation views of type cube—because that’s what they’re referred to in

the SAP HANA certification exam. We use the name calculation views to refer to all three

types of calculation views.


In this chapter, we start by briefly reviewing the different types of data sources

that can be used by calculation views. We then dive into the creation process for

the different calculation views before discussing the various types of nodes you’ll

encounter, including the Semantics node.

Data Sources for Calculation Views

SAP HANA calculation views can use data from a wide variety of sources. We’ll dis-

cuss some of the data sources in later chapters, so don’t worry if you don’t yet

know what all of them are. The main data sources are as follows:

� Tables in the SAP HANA system—both row-based and column-based

The different types of column tables, for example, history column tables and

temporary tables, are all supported.

� SAP HANA calculation views in the SAP HANA system

We can build new calculation views on top of other existing views. For example,

you can use dimension views as data sources in a star join view.

� SQL views

Most databases have the ability to create simple views using SQL code. We dis-

cussed SQL views in Chapter 5.

� Table functions

Table functions are written with SQLScript code and return calculated data. The

format of that data looks similar to the format of data read from a table.

� Virtual tables

In these tables, data from other databases isn’t stored in SAP HANA but is

fetched when and if required.

� Core data services (CDS)

You can use these sources, for example, CDS views, to deploy table definitions

together with a calculation view to the production system.

� Data sources from another tenant in the SAP HANA system

These data sources are available if SAP HANA is set up as a multitenant database

container (MDC) system. These can include tables, SQL views, and calculation

views from another tenant, provided that the proper authorizations are in

place.


Calculation Views: Dimension, Cube, and Cube with Star Join

We create calculation views using graphical modeling. The aim is to do as much

modeling work as possible with graphical calculation views. As SAP HANA has

matured, there has been less emphasis on writing code when creating information

models.

In this section, we’ll walk through the steps for creating these calculation views.

We’ll use the SAP HANA Interactive Education (SHINE) demo application and,

where possible, give you the name of the modeling objects we used. You can use

this to follow along practically in your own SAP HANA system.

Creating Calculation Views

You create calculation views in your Workspace in SAP Web IDE for SAP HANA.

Select and right-click on the SAP HANA database module and folder in which you

want to create the calculation view. You’ll see a context menu like the one shown

in Figure 6.1.

Figure 6.1 Context Menu for Creating SAP HANA Calculation Views


Click New • Calculation View. The options for creating different types of calcula-

tion views appear in the popup dialog box, as shown in Figure 6.2.

Figure 6.2 Creating Calculation Views in SAP Web IDE for SAP HANA

At the top of the dialog box, give the calculation view a Name and a Label (descrip-

tion). Use the Data Category dropdown list and the With Star Join checkbox to cre-

ate the different types of calculation views.

The following instructions explain how to create each type of calculation view

(except for calculation of type time dimension 4, which we will not go into detail

on here):

1 Calculation view of type cube

Select the CUBE option in the Data Category dropdown list, but don’t select the

With Star Join checkbox. The default view node will be an Aggregation node.

These views can also be used for multidimensional reporting and data analysis

purposes.


2 Calculation view of type cube with star join

Select the CUBE option in the Data Category dropdown list, and select the With

Star Join checkbox. The default view node will be a Star Join node. These views

can be used for multidimensional reporting and data analysis purposes.

3 Calculation view of type dimension

Select the DIMENSION option in the Data Category dropdown list. The default

view node will be a Projection node. These views don’t support multidimen-

sional reporting and data analysis. You can use dimension calculation views as

data sources for other calculation views.

Click the Create button to create the calculation view.

If you select the DEFAULT option in the Data Category dropdown list, you’ll create

a type of calculation view that can only be used internally in SAP HANA or con-

sumed by SQL. An external client tool—for example, a reporting tool—won’t be

able to use this calculation view because the DEFAULT calculation view doesn’t cre-

ate the hierarchy views used by the reporting tools when you build the calculation

view. If you’re not using hierarchies, or the calculation view will only be used by

reporting tools that can’t use hierarchies, you can use the DEFAULT calculation

view type.

SPS 04

To be clearer on what the limitation is of the DEFAULT calculation view type, it’s now

named SQL Access Only in SAP HANA 2.0 SPS 04.

Creating Time Dimension Calculation Views

You can also create a special type of dimension view, called a time dimension view.

When creating a dimension calculation view, you can specify the Type as Time, as

shown in the bottom-right corner of Figure 6.2, shown previously. Additional

fields then become available, where you can specify the Calendar (Gregorian or Fis-

cal) and the Granularity (e.g., yearly).

In Figure 6.3, we see an example of a time dimension view.

Note

This example is found in the SHINE demo application; the calculation view is called

TIME_DIM. (The example here was modified to a time dimension view.)


Figure 6.3 Creating Time-Based Calculation Views in SAP Web IDE for SAP HANA

The time-based calculation view adds a time dimension to information views.

Remember that dimensions contain attributes that describe a transaction. If we

add a time and date when the transaction happens, we’ve added another attribute

(time). These time attributes are stored in a time dimension. We can use this to

enhance the information model with drilldown capabilities on a time hierarchy.

We’ll discuss hierarchies in more detail in Chapter 7.

Working with a Calculation View

A calculation view includes different working areas. A calculation view of type

dimension is shown in Figure 6.4 in SAP Web IDE for SAP HANA.

Tip

In SHINE, look for PO_ITEM. The join type should actually be a text join.


Figure 6.4 Working with Calculation Views

Work from left to right in this layout. The left toolbar, called the tool palette, shows

the different type of nodes that are available to you. In the left work area, you’ll see

different view nodes. The top view node is always the Semantics node. In the

Semantics node, you can rename fields to give them more meaning (i.e., seman-

tics). You can also create hierarchies in the Semantics node. (In Chapter 7, we’ll dis-

cuss adding hierarchies to SAP HANA views.)

The node below the Semantics node is called the default view node. The default

node is the topmost node used for modeling in the view. Figure 6.5 illustrates the

default nodes for the different types of calculation views:

1 Type dimension

2 Type cube with start join

3 Type cube

You can add other types of nodes below the default node; for example, Figure 6.4

shows two Join nodes below the default Projection node of the dimension view.

Nodes with semanticsnode on top

Properties

Details of the selected node,for example tables and joins

Navigation

Select nodes

Output fields

Tab menu


Figure 6.5 Default View Nodes for Different Types of Calculation Views

In the left screenshot for the dimension view in Figure 6.5, you can see the names

of the available nodes in the tool palette toolbar. This toolbar is normally collapsed

as shown in the other two examples.

SPS 04

The right side shows what the toolbar looks like in SAP HANA 2.0 SPS 04. The redesigned

icons will keep their high-resolution quality, even when zoomed in.

Note

The Non-Equi Join, the Hierarchy Function, and the Anonymization icons in the toolbar

are new in SAP HANA 2.0 SPS 03. If you don’t see these icons in your system, edit the hid-

den file named .hdiconfig. The top entry should be "plugin_version" : "2.0.30.0".

Tip

Always work bottom-up when building SAP HANA calculation views: start at the nodes at

the bottom and work your way up to the default node. The output of each node becomes

the input of the node above it. The final output of the view, which is the output of the

default node, is shown in the Semantics node.

When you select a View node, the information for the selected node is shown in

the area on the right. In Figure 6.4, shown previously, we selected the second Join


node. In the middle area on the right side, you can see the tables in this Join node

and how they are joined together.

When you select the Columns tab in Figure 6.4, you can see all the selected output

fields of that Join node. In the PROPERTIES section below the output, you can also

see the properties of whatever object, such as a field or join, you’ve currently

selected in the working area.

Adding Nodes

You can add new nodes to the SAP HANA information view by selecting a node

type from the tool palette on the left side and dropping it onto the work area. Drag

the node around on the work area to align it with other nodes. When the node lines

up with another node, an orange line indicates that the node is aligned (see Figure

6.6). Link the nodes by selecting the node, grabbing the round arrow button, and

dragging it to the circle at the bottom of the node above it.

Figure 6.6 Adding and Aligning Join Nodes to the Dimension View

Adding Data Sources

After you’ve added a node to the work area, you can add data sources to it. Select

the Join node. A popup overlay menu with a green plus sign and a tooltip (Add Data

Source) will appear, as shown in Figure 6.7. Click on the plus sign. A dialog box will

appear asking you which data sources you want to add to the node. Enter a few

characters of the table’s name to search for any data sources in the SAP HANA

database module that match those characters. Figure 6.7 shows the list of tables


matching the search for employee data sources. Then, select the data sources, and

click on Finish to add them to the node.

If you click on the Create Synonym button, you can specify the details of the syn-

onyms required. This is part of security (Chapter 13). If you just click on Finish after

selecting the data source, the synonyms are created without asking you for details.

Figure 6.7 Adding Data Sources to a View Node

Creating Joins

After you’ve added the data sources to the data foundation, you’ll want to join the

data sources together. Create a join by taking the field from one table and drop-

ping it onto a related field of another table. Figure 6.8 shows what this looks like in

SAP Web IDE for SAP HANA.


Go to the PROPERTIES area to select the Join Type from the dropdown 1, and set the

Cardinality 2. Make sure you have the join (the line between the tables) selected as

shown; otherwise, the properties won’t show the correct information.

Figure 6.8 Creating a Join and Setting Properties in SAP Web IDE for SAP HANA

Tip

When you create a join, get into the habit of always dropping the field from the main

table onto the related field of the smaller lookup table.

For some join types, it makes no difference in which direction you do the drag and drop.

With a text join, however, it matters.

The join types that are available in calculation views are as follows:

� Referential join

� Inner join

� Left outer join


� Right outer join

� Full outer join

� Text join

� Dynamic join

� Spatial join

Selecting Output Fields

The next step is to select the output fields of your View node. Select the Mapping

tab, as shown in Figure 6.9. Drag and drop the fields from the Data Sources on the

left to the Output Columns on the right. The data sources are stacked vertically, so

if you don’t see the field name you’re looking for, just scroll down until you find it.

Figure 6.9 Selecting Fields as Output Columns for the View Node’s Output

Figure 6.10 shows what a list of output fields looks like in SAP Web IDE for SAP

HANA.


Figure 6.10 List of Output Fields for the View Node

When you select one of the fields in the output columns, you can see the proper-

ties of that field in the PROPERTIES section underneath the Columns tab area (refer

to the overview screen in Figure 6.4). You can see the Name and Data Type of the

column in Figure 6.11. You can change column names to make them more mean-

ingful for end users and reporting tools. You can see this in Figure 6.10, where the

EMPLOYEEID field’s Name is selected. The Mapping property ensures that SAP

HANA still knows what the original field is in the data source.

Figure 6.11 Properties of Output Fields

You can also change the data type, specify whether the attribute is a key attribute,

or determine whether the field should be hidden or not.


Often, you’ll want to select all the fields coming from a lower node as the output

fields of the node. In this case, right-click the name of the data source, and select

Add To Output, as shown in Figure 6.12.

Figure 6.12 Selecting All Fields from the View Node as Output Fields

An alternative way of selecting output fields is to drag the name of the data source

to the Output Columns. All the fields in the data source will be added to the Output

Columns on the right.

The Keep Flag

When we take another look at Figure 6.10, we see that the right-side column is

named Keep Flag. This flag is available for each output column in any of the View

nodes, but it’s particularly important when we aggregate data. In older SAP HANA

systems, this was called the Calculate Before Aggregation flag.

We use this flag when the level of granularity has an impact on the outcome of a

measure, that is, when the order in which you do things matter. Let’s look at an

example by considering the sales data in Table 6.1.


If we first add the columns, we get $55 for the cost per item column and 110 for the

number of items column. When we then multiply these values, we get an aggre-

gate value of $6,050, which isn’t correct.

We should first do the multiplication because a cost per item value is only relevant

for that specific item. When we multiply the values first, we get a cost of $500 for

both rows. When we now create the aggregate (sum), we get the correct value of

$1,000.

This is applicable to formulas where the sequence in which you do them makes a

difference, for example, with multiplication and division. In this case, you should

do the calculations before creating the aggregate.

Practically, it means that you’ll put the following formula in a View node below the

Aggregation node. A Projection node is a good choice for this.

Cost per Item × Number of Items

Finalizing the Calculation View

In Chapter 5, we discussed how to build SAP HANA information views in layers,

with each node building on the previous ones. After you’ve finished building the

calculation view, there are three final steps to follow:

1. Click the Save button above the Workspace area (see Figure 6.13 1). You can also

select the Save option from the File menu 1, or press (Ctrl)+(S) on your key-

board.

2. You then need to Build the calculation view. (We discussed the entire build pro-

cess in Chapter 4.) You can select Build Selected Files from the File menu, the

Build menu, or from the context (right-click) menu 2.

3. Preview your data by selecting the Data Preview option from the menu. (We dis-

cussed the preview functionality in Chapter 4.)

Month Cost per Item Number of Items

January $50 10

January $5 100

Table 6.1 Cost per Item Data Needing the Keep Flag


Figure 6.13 Save and Build the Calculation View

Congratulations! You now know how to create calculation views in SAP HANA.

Working with Nodes

In Chapter 5, we looked at the modeling concepts of joins, star joins, projection,

ranking, aggregation, and union. All of these options are available in calculation

views as View nodes. You can add these View nodes from the tool palette toolbar

on the left of the main work area, as shown earlier in Figure 6.12. These are the basic

building blocks to use in the different layers of your calculation views.


Let’s take a quick look at the different type of nodes, working in the order they

appear in the tool palette toolbar.

Join Node

The earlier examples shown in Figure 6.4 and Figure 6.12 used a Join node. Join

nodes are used at the bottom layers to get data sources into the view. Join nodes

have only two inputs, so if you want to add five tables in the calculation view,

you’ll need four Join nodes. Because of this, joins tend to be highly stacked. (You

can see an example later in this chapter in Figure 6.17.)

To alleviate this pain somewhat, multi-join capabilities were added to Join nodes

in SAP HANA 2.0 SPS 03. You can now add three tables into a Join node, as seen in

Figure 6.14. In the Join node properties, you can specify which one of the three

tables is the Central Table, and if the Multi Join Order is Outside In or Inside Out.

Figure 6.14 A Multi-Join

When there were just two tables per Join node, you always knew which tables got

evaluated first, as the processing is always bottom-up. The tables in the lower node


get evaluated first. When using the new multi-join functionality, you have to spec-

ify the priority sequence. When you specify Outside In, the join furthest from the

central table is executed first, whereas with Inside Out, the closest join is executed

first.

There are cases where the result set is the same regardless of the order in which

joins get executed, for example, when all the joins used are inner joins. However,

if you mix inner joins and left outer joins, the result set can vary depending on the

join execution sequence.

Non-Equi Join Node

Another new feature that was introduced with SAP HANA 2.0 SPS 03 is the Non-

Equi Join node. In this case, the join condition isn’t represented by an equal opera-

tor, but by comparison operators such as greater than, less than, less and equal to,

and so on.

So when would you use a non-equi join? In Figure 6.15, we’re looking at orders.

Figure 6.15 A Non-Equi Join Node


The left table contains the order information, and the right table contains the

order items. These tables are linked by the PurchaseOrderID field. For auditing

purposes, we might want to know which orders were updated after the delivery

date. We create a new join link between the tables with a non-equi join where the

History.ChangeDat field is later than (greater than) the DeliveryDate field.

You’ll notice in Figure 6.15 that the Join Type in the PROPERTIES area is an Inner join.

You can use inner, left outer, right outer, and full outer join types. The Operator

property for the join should be Greater Than in this case. You can set this property

for each non-equi join pair.

Tip

For non-equi joins, you can’t set additional flags, for example, to specify dynamic joins.

Union Node

In Chapter 5, we explained the difference between a standard union and a union

with constant values. Figure 6.16 shows a union with four data sources in the Map-

ping tab. These sources are combined into a list of output fields on the right side.

Tip

Look in SHINE for the Customer_Discount_by_Ranking_and_Region calculation view.

From the context menu of a field in the list of Output Columns on the right, you

can select the Manage Mappings option to open the Manage Mapping popup,

where you can edit the current mappings. You can also set up a Constant Value for

one of the data sources. In this case, it will change the standard union to a union

with constant values. In Figure 6.16, you can see this in action as we have four dif-

ferent SALES columns that were created as a result—one for each region.

Tip

Union nodes in SAP HANA can have more than two data sources. In contrast, Join nodes

can only have two data sources (see Figure 6.17), or three data sources in the case of a

multi-join.


Figure 6.16 Union Node

The Constant Value column in the Manage Mapping popup can be used, for exam-

ple, to identify each data source of a Union node. This is especially useful in the

case of a standard union to identify from which data source each record originates.

A constant value of Y for table Y and Z for table Z will show either of these constant

values in a Constant field.

We can use the Constant Value column for pruning (trimming) of data. Let’s take

the example where you combine data from two data sources using a union. One

data source has the historical data, whereas the other data source has the current

data. By specifying constant values of Historical and Current, respectively, you can

create queries where you can specify where the data should come from. By speci-

fying the data must be from the table where the Constant Value is Current, the

Union node will ignore the historical data. This improves the performance of the

calculation view.


Figure 6.17 Two Inputs Allowed for a Join Node, and Multiple Inputs Allowed for a Union Node

SAP HANA includes even more performance enhancements for the Union node.

You can make unions run faster by using a pruning configuration table. In this

table, you can specify pruning conditions (e.g., filters) to be applied to some col-

umns. For example, a pruning condition can specify that the year must be 2019 or

later. This filters the data and complies with our first modeling guideline: limit (fil-

ter) the data as early as possible. You can specify the Pruning Config Table in the

Advanced section of the Semantics node.

When you select the data source of a Union node in the Mapping tab, you can see

the Properties of that data source in the area below, as shown in Figure 6.18. You’ll

notice that we also have an Empty Union Behavior selection.


Figure 6.18 Empty Union Behavior

By default, we don’t expect any data returned from a data source if the query

doesn’t need data from it. Sometimes, however, it might be useful to know that a

certain data source didn’t return any data. To activate this, you have to set a Con-

stant Value for each data source to uniquely identify it. You then choose Row with

Constants for the Empty Union Behavior. When no data is expected from a data

source, it will still return one record where all fields have null values except the

unique Constant Value that identifies the data source. We can use this to identify

data sources that didn’t return any data to our query.


SPS 04

While we’re looking at the Mapping tab of the Union node, we can quickly note a new

feature of SAP HANA 2.0 SPS 04. In Figure 6.18, you see the PROPERTIES of the second

data source. When you click on the Union node itself, the PROPERTIES section will show

the options you can set for the entire node. In SPS 04, you have a new option called Parti-tion Local Execution, as shown in Figure 6.19. This enables you to control the level of par-

allelization based on processed table content, for example, by perform currency

conversion or aggregation in parallel for every country or time period in a query.

Figure 6.19 Enabling Parallelization in a Node in SAP HANA 2.0 SPS 04

You start the parallelization process by selecting the column that controls the level of

parallelization at the lowermost Union node. The level of the parallelization is based on

data, as a parallel thread is started for each distinct value of the specified column. If you

don’t select a column, the parallelization uses table partitioning.

In a later (higher) Union node that is fed by only one source, you switch it off again.

You have to switch the parallelization on and off in the same view; it won’t work across

views. You can only have one parallelization block per view.

Intersect and Minus Nodes

In SAP HANA, we have two more operations we can perform on the result sets of

the left and right tables. Instead of just combining the two result sets in a union,

we can use an Intersect node to find out which records occur in both result sets.

Alternatively, we use a Minus node to find out which records occur only in one of

the two result sets, eliminating records that occur in both nodes or only in the

other node.

If you remember the marketing campaign in the previous chapter where you

wanted to sell gaming headphones to customers. In this case, you might want to

target only those customers who bought both a computer and a large wide-screen

monitor. You’ll have one result set with customers who bought computers, and

another result set with customers who bought large wide-screen monitors.


Instead of using a union, you’ll now use an Intersect node for use in your market-

ing campaign. Figure 6.20 shows this example, where the Category from Projec-

tion_1 is computers, and the Category from Projection_2 is large wide-screen

monitors. Note that both these projections get their data from the same Join node.

Each projection then uses a unique filter to get the required information.

Figure 6.20 An Intersect Node

However, if your marketing campaign is to sell such monitors to customers who

bought computers but have not yet bought screens, you would use a Minus node.

Another example could be where you want to find all orders that haven’t been

invoiced yet.

Graph Node

The Graph node is an interesting addition to the tool palette. It delivers functional-

ity that you would not normally expect in information modeling. (We’ll discuss

graph modeling in Chapter 11.) Graph modeling is used when you want to analyze

relationships between entities, for example, analyzing who are currently “friends”

on Facebook, and using that to calculate what the chances are of two unrelated

people becoming Facebook friends in the future.


For this, you’ll need a table with people on Facebook, and another table with the

existing “friends” relationships between these people. You then need to create a

graph workspace linking these two tables.

The Graph node must always be the bottom (leaf) node in a calculation view, as

shown in Figure 6.21. It uses the graph workspace as its data source. In addition,

you have to ensure that the referential integrity remains on your data when using

graph modeling.

In the SAP HANA graph reference guide on SAP Help, they use the example of rela-

tionships between various gods and deities in Greek mythology.

Figure 6.21 Graph Node

Projection Node

Projection nodes can be used to remove some fields from a calculation view. If you

don’t send all the input fields to the output area, the fields that aren’t in the output

area won’t appear to client tools.

You can also use Projection nodes to rename fields, filter data, and add new calcu-

lated columns to a calculation view. (We’ll discuss filter expressions and calculated

columns in Chapter 7.)


Aggregation Node

The Aggregation node in calculation views can calculate single summary values

from a large list of values; for example, you can create a sum (single summary

value) of all the values in a table column. These summary values are useful for

high-level reporting, dashboards, and analytics. (The functionality is similar to the

GROUP BY statement in SQL.) The Aggregation node in calculation views also sup-

ports all the usual functions: sum, count, minimum, maximum, average, standard

deviation, and variance. For performance reasons, be cautious when using the last

three functions (average, standard deviation, and variance) on very large stacked

calculation views.

You normally calculate aggregates of measures, but in SAP HANA, you also can cre-

ate aggregates of attributes, which basically creates a list of unique values.

Tip

Watch out when creating average, standard deviation, and variance aggregates in

stacked scenarios as this could affect the performance of your calculation views.

Rank Node

We can use Rank nodes to create top N or bottom N lists of values to sort results. In

the Definition tab of a Rank node, you can access extra properties to specify the

Rank node behavior.

Figure 6.22 shows the Rank node area. The Sort Direction field allows you to specify

whether you want a top N or bottom N list. The Order By field allows you to specify

what you want to sort the list by, such as the Product_Price. In this case, the top 10

most expensive items will be shown.

The Threshold field specifies how many entries you want in the list. If you specify a

Fixed value of “10”, you’ll produce a top 10 or bottom 10 list. You can also choose to

set this value from an Input Parameter, which means that the calculation view will

ask for a value each time a query is run on this view. (We’ll discuss input parame-

ters in Chapter 7.)

You can also partition the data by a specific column. In the example in Figure 6.22,

we selected the Country column. In this case, we’ll get the top 10 most expensive

items for each country.


Figure 6.22 Rank Node

By default, these partition columns are always used. When you select the Dynamic

Partition Elements checkbox, SAP HANA will ignore any of the specified partition

columns that aren’t included in the query. In other words, if your query doesn’t

include the Country column (in Figure 6.22) in the query, SAP HANA will ignore the

Country partition column.

Finally, and most importantly, you can specify that a new output column must be

generated. You can then use the column created by Generate Rank Column in

analysis or reporting tools to sort the results.

SPS 04

The Rank node is the one node that had the biggest update with SAP HANA 2.0 SPS 04.

Just do a quick compare of Figure 6.22 (SPS 03) with Figure 6.23 (SPS 04).

� The Generate Rank Column and the Logical Partition area are still the same.

� The Order By column was expanded to a Sort Column area to allow for multiple sort

columns.

� The Sort Direction is renamed to Result Set Direction.

� The Threshold value is now Target Value.


Figure 6.23 Rank Node in SAP HANA 2.0 SPS 04

There are three new values as well: Aggregation Function, Result Set Type, and Offset.

The Aggregation Function is where most of the new functionality lies. This gives you

additional ranking functionalities that allow you to select records based on relevance, for

example, select the last value for each day. There are four options, as shown by the drop-

down menu:

� RowThis aggregation function automatically numbers the rows within a partition of a

result set in sequence, without taking any additional factors into account. The first row

of each partition is assigned a rank of 1. If two numbers are the same, there is no guar-

antee of which one will be consistently ranked before another.

� RankThis option is the same as that available in SPS 03.

� Dense RankThis function is closely related to the default Rank function. The difference comes in

when two records in the list have the same ranking values. The default Rank function

uses “Olympic” ranking. If two competitors have the same scores, they share a place,

but a gap is left. For example, if two people have the same score on the second position,


they both have a rank of 2. There is then a gap (there is no rank of 3), and the next posi-

tion has a rank of 4. With the Dense Rank function, the next position won’t have a rank

of 4, but of 3. There are therefore no gaps in the ranking.

� SumThe Sum function will keep showing records until the sum of the records is below the

target value. You specify a range by setting the Target Value and Offset values. The

range will start where the Rank Column value is larger than the Offset value, and will

continue while the Rank Column value is less or equal to the Offset value + the TargetValue. This can be used to report data on quantiles, such as quartiles. The Sum function

will only use the first Sort Column.

The Result Set Type is based on either the rank number (Absolute) or on the percentage(a value between 0 and 1).

If you want to ignore the first five values in a ranking, you simply specify an Offset of “5”.

In this case, we use a Result Set Type based on the rank number (Absolute).

Table Function Node

A Table Function node is code written in SQL or SQLScript which returns a result set

that looks and behaves as if it came from a table with data. We’ll discuss table func-

tions in more detail in Chapter 8.

Hierarchy Function Node

The Hierarchy Function node is a new feature in SAP HANA 2.0 SPS 03. Hierarchy

functions enable you to build information models with hierarchical data stored in

tables. Such tables typically have data records arranged in a tree or directed graph.

We’ll discuss hierarchies and hierarchy functions in the next chapter.

Anonymization Node

The Anonymization node is also a new feature in SAP HANA 2.0 SPS 03.

A lot of business data contains personal or sensitive information, and many large

corporates seem to show very little respect for your data. They seem reluctant to

get rid of this data as they want to use it for statistical analysis. The anonymization

methods in SAP HANA 2.0 SPS 03 still allow you to gain statistically valid insights

from your data but also protect the privacy of individuals. It works by modifying

individual records without changing the aggregated statistics. For example, you


can “scramble” salary data so that no one can find out a particular individual’s sal-

ary, but the aggregates such as sum, average, and so on still stay the same.

A method such as k-anonymity is an intuitive and widely used method for modi-

fying data for privacy protection. It hides the individual record in a group of simi-

lar records, thus significantly reducing the possibility that the individual can be

identified.

The topic of data privacy is often treated as a security topic and will therefore be

discussed in Chapter 13.

Star Join Node

The Star Join node, which is the default View node in star join views, is used only to

join a data foundation with fact tables to dimension views.

Tip

The join types available in the Star Join node are the same as the normal joins in a Joinnode, for example, referential joins. It’s what they’re joining that differs.

Figure 6.24 shows a Star Join node.

Note

Look in SHINE for the Test calculation view.

The middle block in the Join Definition tab is the data foundation, which comes

from a Join node with two fact tables. The other blocks are calculation views of type

dimension.

Note

Only calculation views of type dimension are supported as the dimension data sources in

the Star Join node of a calculation view.

The Star Join node is the default node of calculation views of type cube with star

join. Use Join nodes, shown as Join_1 in Figure 6.24, to join all the transactional

tables together. The uppermost Join node contains all the measures. The output

from this last Join node becomes the data foundation for the Star Join node. Figure

6.24 only has one Join node called Join_1 for the data foundation.

Then add the required dimension views, which have to be calculation views of type

dimension, into the Star Join node and join them to the data foundation.


Figure 6.24 Star Join Node

The star joins between the fact tables and dimensions can use the standard join

types, for example, inner joins, referential joins, left outer joins, and even full

outer joins.

Tip

A full outer join can be defined only on one dimension view used in a Star Join node, and

this dimension view must appear last, just before the Star Join node.

Tip

You can only create temporal joins in a Star Join node and by specifying the join as an

inner join. In that case, a new section appears below the join properties where you can set

the Temporal Properties.

Semantics Node

Semantics is the study of meaning. In the Semantics node, you can give more

meaning to the data itself. One basic example is renaming fields. Some database


fields have really horrible names—but in the Semantics node, you can change the

names of these fields to make them more meaningful for end users. This is espe-

cially useful when users have to create reports. If you have a reporting background,

you’ll know that a reporting universe can fulfill a similar function.

The Semantics node includes four different tabs, as shown in Figure 6.25. We’ll only

discuss the first two tabs in this chapter; the other two tabs will be discussed in

Chapter 7.

Figure 6.25 Tabs in the Semantics Node

View Properties Tab

The first tab in the Semantics node is the View Properties tab. Here, as shown in

Figure 6.26, you can change the properties of the entire calculation view. The View

Properties tab has two subtabs, called General and Advanced, as shown in Figure

6.26.

In the Data Category property, you can see the type of calculation view. In this case,

it’s a DIMENSION view. In addition, the Default Client property is very specific to

data from SAP Business Suite systems.

In such SAP NetWeaver systems, we work with the concept of different clients. This

use of client doesn’t refer to a customer or as part of client/server. In this case, we’re

talking about an SAP system database being subdivided into different clients.

When you log in to an SAP NetWeaver system, it asks not only for your user name

and password but also for a client number. You then are logged into the corre-

sponding client. You can only see the data in that client—not everything inside the

SAP database. You can think of a client in SAP systems as a “filter” that prevents

you from seeing all the data.

Because SAP HANA is used as the database for many different SAP systems, SAP

HANA must also allow for this concept of limiting data to a specific client. By set-

ting the session client, you can say what the default client number is for this calcu-

lation view.


Figure 6.26 View Properties Tab in the Semantics Node


The Session Client option will filter the data to display only data from the specific

SAP client you’re using for your session, for example, from a reporting tool.

Instead of using one of these two options, you can also explicitly specify the SAP

client number that you want to display data for, for example, “800”, by selecting

the Fixed Client option.

Tip

Settings in the Semantics node of a calculation view takes precedence over client set-

tings made elsewhere, for example, the default client property for an SAP Web IDE user.

For the Apply Privileges setting, you can choose between no privileges or SQL Ana-

lytic Privileges. (We’ll discuss these options in more detail in Chapter 13 when dis-

cussing security.) We recommend always using SQL Analytic Privileges for

production systems.

Setting the Deprecate checkbox doesn’t mean that the calculation view won’t work

any longer; it will merely warn people that this calculation view has now been suc-

ceeded by a newer version somewhere in the system and that they should use the

newer version because this one will disappear someday in the future. When you

add this calculation view in another data model, you’ll see a warning not to use

this calculation view. The calculation view will still work as usual, however.

In the Advanced subtab, you can set the Execute In to SQL Engine. You’ll only set

this preference when developing SAP HANA Live calculation views. More informa-

tion on SAP HANA Live calculation views can be found in Chapter 14. Normally you

shouldn’t set any of the Advanced properties.

Columns Tab

The next tab in the Semantics node is the Columns tab (see Figure 6.27). In this tab,

the Type column shows whether the field is a measure or an attribute. This is very

helpful to analytics and reporting tools to ensure that the data is shown and

manipulated in the appropriate formats; for example, numeric fields (measures)

can be aggregated.

Renaming fields is easy. In the Columns tab, click the Name of the field, and type

the new name. The same rule applies for the Label. The label is used, for example,

when creating SAP HANA extended application services, advanced model (SAP

HANA XSA) and HTML5 applications, or in some reporting tools.


Figure 6.27 Columns Tab in the Semantics Node

Renaming fields is more common than you might expect. You tend to use field

names like Price in an isolated context when you create tables for order, sales, or

discounts. When you later start using these tables together, for example, in a Star

Join node, you’ll have multiple fields called Price. SAP HANA will helpfully offer to

give these different fields an alias, which is a different name for each of the price

variations. You can create an alias such as OrderPrice, or the SAP HANA system will

create an alias such as Price_1 for you.

You might want to hide fields if you don’t want to show the original field to the

end user. Even if a field is Hidden, it can be still used in a formula or a calculation;

the field will still be available internally in SAP HANA for the calculation view but

won’t be shown to the end users.

In the Semantics column, you can further specify the type of field. An Assign

Semantics dialog box will appear where you can select the type of field. These types


are mostly specific to SAP HANA, and they can range from currency to dates to spa-

tial. We’ll look at these semantic types in more detail in Chapter 7.

Below the Columns tab name, you can see a toolbar as shown in Figure 6.28. The

first three icons are used to mark the fields as attributes or measures. The fourth

icon, as illustrated, is used to extract semantics. Let’s assume you’ve created a few

dimension views with some hierarchies. You now create a new star join view,

where you use the dimension views. You can use the Extract Semantics feature to

replicate the hierarchies from the underlying dimension views. With this feature,

you don’t have to create similar hierarchies in the star join view, which will save

you a lot of time.

You can also use it to copy labels, label columns, aggregation types, and semantic

types from the referenced (underlying) calculation view, as shown in Figure 6.28.

Figure 6.28 Extracting Semantics


Lastly, let’s take a quick look at the Show Lineage option accessed via the fifth icon

on the toolbar (Figure 6.29).

To use this option, select an output column, and click on the Show Lineage icon.

The left side shows in each node where the column occurred. Go down the layers

of nodes, and you can see that the Supplier_Country column was introduced by the

Addresses table in the second Join node. This works even if the column got

renamed along the way.

Figure 6.29 Using the Show Lineage Option to Find the Node an Output Column Originated From


In this chapter, the following terminology was used:

� Data sources

SAP HANA information views can use data from a wide variety of sources. The

main data sources are as follows:

– Row-based and column-based tables


– SAP HANA information views in the SAP HANA system

– Decision tables

– SQL views

– Table functions

– Virtual tables with data from other databases

– CDS

– Data sources from another tenant in the SAP HANA system, if SAP HANA is

set up as an MDC system

� Aggregation node

Used to calculate single summary values from a large list of values; supports

sum, count, minimum, maximum, average, standard deviation, and variance.

� Graph node

Used for modeling relationships between entities and querying calculations,

such as the shortest path between two entities.

� Join node

Used to get data sources into information views.

� Projection node

Used to remove some fields from a calculation view, rename fields, filter data,

and add new calculated columns to a calculation view.

� Rank node

Used to create top N or bottom N lists of values to sort results.

� Star join node

Used to join a data foundation with fact tables to dimension views. The join

types available in the star join node are the same as the normal joins in a join

node. It’s what they’re joining that differs.

� Union node

Used to combine multiple data sources. You can make unions run even faster

by using a pruning configuration table.

� Intersect node

Used to find the records that occur in all the data sources.

� Minus node

Used to find the records that are found only in one of the data sources.

� Semantics node

Used to give meaning to the data in information views.


� Calculation view of type dimension

Information views used for master data.

� Calculation view of type cube with star join

Views used for multidimensional reporting and data analysis purposes.

� Calculation view of type cube

Views used for more powerful multidimensional reporting and data analysis

purposes.

Practice Questions









1. For what type of data are SAP HANA calculation views of type dimension

used?

� A. Transactional data

� B. Master data

� C. Materialized cube data

� D. Calculated data

2. Which of the following node types can you use to build a dimension view?


� A. Star Join node

� B. Aggregation node

� C. Data Foundation node

� D. Semantics node

� E. Union node


3. Which types of calculation views can you create? (There are 2 correct answers.)

� A. Standard

� B. Derived

� C. Materialized

� D. Time-based

4. In which direction do you build calculation views when including the source

tables in Join nodes?

� A. From the Join nodes up to the Semantics node

� B. From the Semantics node up to the default node

� C. From the Join node down to the default node

� D. From the Semantics node down to the Join nodes

5. You rename a field in the Semantics node to add clarity for end users. What

does SAP HANA use to keep track of what the original field name is?

� A. The label column property

� B. The mapping property

� C. The label property

� D. The name property

6. You want to use a field in a formula but don’t want to expose the values of the

original field to the end users. What do you do?

� A. Make the field a key column.

� B. Change the data type of the field.

� C. Rename the field.

� D. Mark the field as hidden.

7. Where can you set the default value for the SAP client for the entire calculation

view?

� A. In the Join node of the calculation view

� B. In the semantic layer of the calculation view

� C. In the default node of the calculation view

� D. In the Preferences option of the Tools menu


8. True or False: When you mark a calculation view as deprecated, that view no

longer works.

� A. True

� B. False

9. What node can you use for sorting results, for example, to show the top 100?

� A. Intersect node

� B. Projection node

� C. Rank node

� D. Union node

10. For what SQL keyword is the Aggregation node the equivalent?

� A. ORDER BY

� B. GROUP BY

� C. DISTINCT

� D. UNIQUE

11. What is the default node for a calculation view of type cube?

� A. Aggregation node


� C. Star join node

� D. Join node

12. What node is used to link dimension views to a data foundation (fact tables)?

� A. Join node


� C. Union node

� D. Star Join node

13. What data source is available for Join nodes in calculation views?

� A. All procedures in the current SAP HANA system

� B. Scalar functions from all SAP HANA systems


� C. Calculation views from other tenants in an MDC system

� D. Web services from the Internet

14. Which join types are available in a Join node of a calculation view? (There are

2 correct answers.)

� A. Referential joins

� B. Spatial joins

� C. Cross joins

� D. Temporal joins

15. From where does the extract semantics feature get the definition of hierar-

chies?

� A. From the current calculation view

� B. From the table data sources

� C. From the underlying dimension views

� D. From the SQL view data sources

16. You want to see in a Union node which data sources return no results for a

query. How do you achieve this?

� A. Set the is Null property in the Manage Mapping dialog.

� B. Create a union pruning configuration table.

� C. Create a standard union.

� D. Set the Empty Union Behavior property.

17. You want to ignore columns defined in the Rank node that aren’t included in

the query. What setting do you set?

� A. Default Client

� B. Keep Flag

� C. Dynamic Partition Elements

� D. Hidden


18. True or False: A table function can be used as a data source for a calculation

view.

� A. True

� B. False

19. True or False: You can use dynamic joins in a Non-Equi Join node.

� A. True

� B. False

20. Where in a calculation view can you add a Graph node?

� A. Only as the top node

� B. Always as the bottom (leaf) node

� C. Anywhere from the bottom (leaf) node to the default node

21. What node uses a special configuration table to optimize performance?

� A. Union node

� B. Aggregation node

� C. Non-Equi Join node

� D. Intersect node



You use SAP HANA calculation views of type dimension for master data.

Transactional data needs a calculation view marked as Cube. SAP HANA doesn’t

store or use materialized cube data. You can’t calculate data when you only

work with attributes; you need measures to do that.

2. Correct answers: B, D, E

You use an Aggregation node, a Union node, and a Semantics node to build a

dimension view. Dimension views can be used in a star join, but it doesn’t form

part of the actual dimension view. Data Foundation nodes don’t exist in calcu-

lation views. They used to exist in attribute and analytic views, which have

since been deprecated.


3. Correct answers: A, D

You can create standard and time-based calculation views. Derived views have

been deprecated and were used with attribute views. SAP HANA doesn’t use

materialized views.


You build calculation views (when including the source tables in Join nodes)

from the Join nodes up to the Semantics node. The Semantics node is above the

default node, which is above the Join nodes. You always start from the bottom

and work up.


SAP HANA uses the mapping property to keep track of what the original field

name is when you rename a field in the Semantics node.


Mark a field as Hidden when you want to use a field in a formula but don’t want

to expose the values of the original field to end users.


You set the default value for the SAP client for the entire calculation view in the

semantic layer of the calculation view. You can’t set properties for the entire

view in the join or default nodes of the calculation view. The Preferences option

in the Tools menu doesn’t contain such a setting.


False. When you mark a calculation view as deprecated, that view will still work.


You use a Rank node to sort results. Projection nodes have calculated columns

(which we’ll discuss in Chapter 7), but aren’t used to sort results, for example, to

show the top 100.

Intersect and Union nodes combine records from multiple data sources.


Aggregation nodes are equivalent to the GROUP BY keyword.


The default node for a calculation view of type cube is an Aggregation node. A

Projection node is the default node of a calculation view of type dimension. A

Star Join node is the default node of a calculation view of type cube with star

join.


A Star Join node is used to link dimension views to a data foundation (fact tables).



Calculation views from other tenants in an MDC system are available as data

sources for Join nodes in calculation views.

Scalar functions don’t return table result sets, and procedures don’t have to

generate any result sets. (For more information, see Chapter 8.)


Referential joins and spatial joins are available in a Join node of a calculation

view. Temporal joins are only available in Aggregation nodes and Star Join

nodes.


The Extract Semantics option can get the definition of hierarchies from the

underlying dimension views.

Table data sources and SQL view data sources don’t have hierarchies.


You set the Empty Union Behavior property if want to see in a Union node which

data sources return no results for a query.

You create a union pruning configuration table for performance reasons.

For the Empty Union Behavior to work, you need to set constant values. A stan-

dard union doesn’t use constant values.


You set the Dynamic Partition Elements checkbox if you want to ignore col-

umns defined in Rank node that aren’t included in the query.

The Default Client setting is used in the Semantics node for the entire calcula-

tion view.

The Keep Flag and the Hidden flag are set for each field in the Output Columns

tab on all nodes.


True. A table function can be used as a data source for a calculation view.


False. You can’t use dynamic joins in a Non-Equi Join node.

Tip

Some questions in the exam will only have three answer options, like the following ques-

tion.



A Graph node must always be the bottom (leaf) node.


Union nodes can use a special configuration table to optimize performance for

union pruning.

Takeaway

You should now know how to create SAP HANA information views and when to

create each type of calculation view based on your business requirements. You’ve

learned how to add data sources, create joins, add nodes, and select output fields.

In Table 6.2, we define the different information views and provide a list of their

individual features.

Summary

You learned how to create calculation views and add data sources, joins, nodes,

and output columns. You added meaning to the output columns in the Semantics

node and set the properties for the view.

In the next chapter, we’ll discuss how to further enhance our information views

with additional functionality, such as filter expressions, calculated columns, cur-

rency conversions, and hierarchies.

Calculation Views of

Type Dimension

Calculation View of

Type Cube with Star Join

Calculation View of

Type Cube

Purpose: Create a dimension

for our analytic modeling

needs

Purpose: Create a cube for

our analytic requirements

Purpose: Create more

advanced information

models

� Treats all data as attri-

butes

� Uses mostly joins

Combines fact tables with

dimensions

Focuses on extra functional-

ity, such as aggregations,

joins, projections, ranking,

and unions

Table 6.2 Information View Features

Chapter 7

Modeling Functions


� Learn how to define calculated and restricted columns

� Examine filter operations and filter expressions

� Identify when to create variables and input parameters

� Understand currency and currency conversions

� Know how to create hierarchies for use in reporting tools

� Use Hierarchy Function nodes to create hierarchies for use inside

the calculation views

Chapter 7 Modeling Functions280

In previous chapters, you learned how to build SAP HANA calculation views. In this

chapter, you’ll see how to enhance calculation views with additional functionality,

including adding output fields, filtering data, converting currencies, asking for

inputs at the time they are run, and implementing hierarchies.

Real-World Scenario

You’re working on an SAP HANA modeling project and are asked to help with

publishing the company’s financial results for the year.

You copy some of the calculation views that you’ve created before and start

adding additional features to them. First, you filter the data to the last finan-

cial year and to the required company code. You then implement currency

conversions on all the financial transactions, so that the results can be pub-

lished in a single currency.

The board members aren’t sure if they want the results in US dollars or in

euros, so they ask you to give them a choice when they run their queries on

the SAP HANA calculation views. They also ask you to create a dashboard

where they can drill down into the results to see any missing data or issues.

Finally, you create an overview report showing the past few years of financial

results side by side.



knowledge of SAP HANA modeling and calculation views, as well as your under-

standing of how to enhance and refine these views with additional functionality.


topics:

� Calculated columns

� Restricted columns

� Filter operations

� Variables

� Input parameters


� Currency and currency conversions

� Hierarchies

� Hierarchy functions

Note

This portion contributes up to 15% of the total certification exam score. This is one of the

largest portions of the certification exam.


In the previous chapter, we saw how to create output columns for our calculation

view. In this chapter, we’ll learn about creating additional output fields. We’ll see

how to add calculated columns, restricted columns, counters, and currency con-

version columns to the list of output fields in our calculation views. In our discus-

sions on these topics, see if you can identity which feature solves each problem

described in the real-world scenario described previously.

Let’s start with the first of our nine topics on how to enhance our SAP HANA calcu-

lation views with modeling functions: calculated columns.

Calculated Columns

Up to this point, we’ve created calculation views using available data sources.

However, quite often, we’ll want SAP HANA to do some calculations for us on that

data (e.g., calculate the sales tax on a transaction).

In the past, this was usually done using reporting tools. However, SAP HANA has

all the data available and can do the calculations without having to send all the

data to the reporting tool. SAP HANA does the calculations faster than reporting

tools and sends only the results to reporting tools, saving calculation time, band-

width, and network time.

When we create calculated columns in SAP HANA, it adds additional output col-

umns to the output of our views. The calculated results are displayed in these cal-

culated columns. Figure 7.1 illustrates how calculated columns work.


Figure 7.1 Calculated Columns Added as Additional Output

On the left side of Figure 7.1 is our source data from a table. We added two calcu-

lated columns on the right. The first calculated column calculates discounts, and

the second one calculates the sales tax.

Note

The calculated columns are part of the output of the calculation view. The results aren’t

stored anywhere, but they are calculated and displayed each time the view is called.

We’ll start by discussing the calculated columns in SAP HANA that behave like

those we just described. After that, we’ll also look at counters, which create calcu-

lated columns for the number of items in specified groupings of data.

Creating Calculated Columns

In SAP Web IDE for SAP HANA, we create calculated columns in the work area of a

node. In the Calculated Columns tab, we select the + option, as shown in Figure 7.2.

When you click on the name of the calculated column, you’ll get the work area. In

the work area, you can enter the various settings for the calculated column. For

example, you have to specify the Name, Label, and Data Type of the new column

you want to create.

You can also specify what Semantic Type you want to assign to your new calculated

columns, for example, making it a Date or a Currency column.

Item Quantity Price Total

Device 2 $200 $400

Plug 500 $5 $2500

Cable 200 $10 $2000

Connector 7 $20 $140

Adapter 3 $50 $150

Discount Sales Tax (VAT)

$380 $57

$2250 $338

$1800 $270

$133 $20

$143 $21

Calculated columns

5% discount, but10% if more than

100 items

15% Sales Tax(VAT)

Table


Figure 7.2 Creating a New Calculated Column in a Node

In the Expression area, you can add your formula, called the expression, for doing

the calculation. You can just type in the formula you need, or use the Expression

Editor screen for more complex expressions. Click on the Expression Editor button

to open the next screen.

In the Expression Editor screen, shown in Figure 7.3, we first specify what program-

ming Language we want to write the expression in. SQL is the default. You can still

use the Column Engine for expressions you might have used in older calculation

views that you’re migrating or upgrading.

The focus is the formula (called the Expression) on the right column.

The three tabs on the left of the screen help us create the expression:

� Elements

The Elements tab contains all available columns, previously created calculated

columns, and input parameters. We can build our new calculated column on

previous calculations to build up more complex calculations.


� Functions

The Functions tab contains various useful functions, such as converting data

types, working with strings, doing the basic mathematical calculations, and

working with dates. We also have some extra functions, for example, the if()

function for if-then-else calculations, but these are only available when the Lan-

guage is set to Column Engine. You can also get some online help when clicking

the i (information) icon.

� Operators

The Operators tab contains operators, for example, for adding, multiplying,

comparing, or combining columns.

Figure 7.3 Using the Expression Editor to Create the Formula for Calculated Columns

All the examples here use measures. However, we can also create calculated col-

umns for attributes as well. The Functions also allow us to work with strings and

dates, so we can create expressions using attributes.


Tip

We recommend that you click on the various components instead of entering the expres-

sion. Although you can enter the expression directly in the text area, we’ve found that

many people either mistype a column name or forget that SAP HANA column names can

be case-sensitive. By clicking on the components instead, SAP Web IDE for SAP HANA

assists you with the typing.

The Expression Editor also helps us with syntax checking when we click the Vali-

date Syntax button on the top right. In the top example in Figure 7.4, the Expres-

sion Editor describes the error. In this case, we used the if() function in a SQL

expression. As it isn’t available here, we get an error. When we change the Lan-

guage to Column Engine, we get a Valid Expression.

Figure 7.4 Syntax Checking in the Expression Editor

Tip

In Chapter 6, we discussed the Keep Flag column, where we mentioned that sometimes

you have to calculate values before you do any aggregation. This is very applicable in the

case of calculated columns. In some cases, you might have to create your calculated col-

umns in the node below the Aggregation node.

Counters

In Aggregation and Star Join nodes, we sometimes want to know how many times

in the result set a certain item occurs for an attribute. For example, we might want

to know how many unique items were sold in each country to find the countries

with the largest variety. We do this using counters.


The option to create a new counter is found in the calculated column’s create (+)

menu in Aggregation and Star Join nodes (see Figure 7.5). In the Calculated Column

tab, click the + icon, and select the Counter option to create a new counter. In our

example, we’ll put the counter on items sold.

We can also use multiple columns in the Counter section to expand our example

to add item categories, for example, to show which country has the largest variety

of items for electronics, the largest variety of items for industrial pumps, and so

on.

Figure 7.5 Creating a New Counter

Restricted Columns

The easiest way to explain restricted columns is to think of a reporting requirement

that you frequently encounter. For example, let’s say we have a website where we


work with the data of who visited the website and what device they used. We use a

view like the one shown in Figure 7.6. Our data from the source table is in the two

columns on the left. We can see how many users connected with each type of

device, organized per row.

We would like our report to be like the bottom right of the screen in Figure 7.6,

where we have a column for each device with the total number of users for each

device. You might know this as a pivot table. We already saw in Chapter 5 that we

could use a union with constant values to achieve a similar result.

We can also achieve this by creating a restricted column for each device. This adds

a new additional output column to our view. When we create a restricted column

for the mobile phones, we restrict our column to only show mobile phone data.

Everything else in that column is NULL. When we now aggregate the restricted col-

umn, we get the total number of users visiting the website on mobile phones. We

create a restricted field for each device to create our report.

Figure 7.6 Creating Four Restricted Columns and Aggregating the Result Sets

Device Number of users

Mobile phone 20

Tablet 10

Desktop 10

Mobile phone 10

Mobile phone 40

Laptop 20

Tablet 20

Laptop 30

Mobile phone 50

Mobile phone 40

Laptop 20

Desktop 20

Mobile phone Tablet Laptop Desktop

160 30 70 30

Mobile phone Tablet Laptop Desktop

20

10

10

10

40

20

20

30

50

40

20

20

Aggregated results

Restricted columnsTable


Tip

Even though a union with constant values can achieve a similar output result, you don’t

always have tables with similar field structures available to do a union. Using restricted

columns to achieve this output result will always work and is easy to set up.

Because we need aggregation for this to work properly, restricted columns are only

available in the Aggregation and Star Join nodes.

Note

We create restricted columns on attributes. For example, it doesn’t make sense to create

a Reporting column with a heading of “25” (measure). However, a Mobile phone column

(an attribute) provides more useful information.

Creating a restricted column is similar to creating a calculated column. In the

Restricted Columns tab, as shown in Figure 7.7, we click the + icon.

Tip

In SAP HANA Interactive Education (SHINE), look for the PURCHASE_COMMON_CUR-RENCY calculation view.

In the right side of the work area, we create our restricted column. We specify the

name of the restricted column and what data we want to display in the column. In

this case, we chose the GROSSAMOUNT column.

In the Restrictions area, we can add one or more restrictions, such as the year has

to be 2019, the device has to be a mobile phone, or only show the Flat screens sold

(Figure 7.7).

If we add multiple restrictions, SAP HANA automatically creates the relevant AND

and OR operators. For example, if we have two restrictions for the year 2019 and

2020, the Expression Editor will use year = 2019 OR year = 2020.


Figure 7.7 Creating a Restricted Column for the Net Amount of Invoices

When we enter a value, such as “2019” for the year, we can ask the Expression Editor

screen to give us a list of existing values currently stored in the system. In Figure

7.8, we asked for a list of item categories. In the list, we see, for example, Flat screens

and Graphic cards. This popup list is called the value help list. You’ll find these value

help lists in all the modeling screens.

In the restricted column creation dialog, in the Restrictions area, we can choose to

use an Expression rather than specify individual Column values. We can then use

the Expression Editor to generate the relevant portion of the SQL WHERE statement

directly.


Figure 7.8 Choosing a Restriction Value from a Predefined List of Values

If you first fill in the values in the Column view and then change to the Expression

view, the Expression Editor automatically converts this for you, as shown in Figure

7.9.

You can also create a restricted column using a calculated column. Just use the

expression if(“Category”=’Flat screens’, “GROSSAMOUNT”, NULL). The if() function

in the Expression Editor works similarly as the IF function in Microsoft Excel: if the

condition (first part with the equal sign) is true, then show the second part; other-

wise, show the last part. Note that the if() function isn’t available in the default

Language, which is SQL. In SAP HANA 2.0, it’s recommended to choose SQL.

Just like with a calculated column, a restricted column doesn’t store data. Each

time a calculation view is used, the restricted column is created and aggregated.

Tip

A restricted column fundamentally differs from a filter or a SQL WHERE condition. If you fil-

ter on the year 2019, all the data in the entire view will be trimmed to only show the data

for 2019. When you create a restricted column for the year 2019, only the data in that

(additional) column is trimmed for 2019.

Let’s look at filters next.


Figure 7.9 Using the Expression Editor to Create Restrictions on Restricted Columns

Filters

Sometimes we don’t want to show all the data from a specific table. For example,

we might only want to show data for the year 2019. In this case, we can apply a fil-

ter. Filters restrict the data for the entire data source. They are therefore similar to

the WHERE condition in an SQL statement. When we apply filters, SAP HANA can

work a lot faster because it doesn’t have to work with and sift through all the data.

Something to keep in mind is that you can leverage partitions when designing fil-

ter expressions. When a field has been partitioned, SAP HANA has already sepa-

rated the data by values. If you create a filter on a partitioned column, you can

expect good performance on your filter expressions.

There are three different ways we can use filtering. We can use filter expressions in

the calculation views, and this is what we’ll use most of the time. The other two fil-

tering methods are specific to SAP systems written on SAP NetWeaver and are

there for compatibility reasons. We’ll start with using filter expressions in our cal-

culation views.


Filter Expressions

To create a filter expression, select the Filter Expression tab in the work area of your

node. This puts you into the Expression Editor, as shown in Figure 7.10, where you

can create an expression.

Figure 7.10 Creating a Filter Expression in a Projection or Join Node

This works similar to the Expression Editor for calculated and restricted columns.

In this case, however, the filter expression will be similar to the WHERE condition of

an SQL statement. A filter icon will appear next to the name of the Filter Expres-

sion tab after you’ve created a filter expression.

In the left work area of your calculation view, where you see the view nodes, you’ll

also see a filter icon in the node, as shown in Figure 7.11.

Figure 7.11 A Filter Expression Icon in the View Node


Filtering for SAP Clients

Filtering of data can be implemented more widely than the examples we’ve just

seen. In Chapter 6, we saw how to set an option for filtering to an SAP client in the

Semantics node. Later, we’ll see another example of limiting data to the session cli-

ent for data from SAP financial systems. Chapter 8 and Chapter 13 also provide fur-

ther details on filtering for an SAP client using SQL/SQLScript and security,

respectively.

Domain Fixed Values

SAP ERP and SAP S/4HANA use the ABAP programming language, which has some

powerful Data Dictionary features. In ABAP, we can define reusable data elements

(e.g., data types) and use them in our table definitions.

Sometimes we want these data elements to only allow a range of values. We define

a domain with the range of values and use this domain with the data element. In

all the tables where that specific data element is used, the users can now only enter

values that are in the specified range.

Instead of a range, we can also specify fixed values for the domain, which are

known as the domain fixed values. These are values that won’t change, and the

users can only choose from the available list when they enter data into the fields.

Let’s look at some examples: Say the users need to enter a gender. The domain val-

ues are fixed to male and female. This is very similar to the value help shown ear-

lier in Figure 7.8. Another example could be the possible statuses of a sales order,

which could be Quoted, Sold, Delivered, Invoiced, and Paid.

The lists of fixed values are stored in tables DD07L and DD07T in the SAP ERP system.

Because these tables are so valuable for business systems, we often replicate (or

import) these tables to SAP HANA. We can then use these values in SAP HANA for

the Value Help screens, similar to that shown earlier in Figure 7.8.

These domain fixed values can now also be used to filter business data; for exam-

ple, we can filter the sales data to the domain fixed value of paid to see only the

sales that are already paid.

Variables

In the previous section, we looked at filtering. These filters were all static, in the

sense that we couldn’t change them after we specified them. Each time you query

the view, the same filters are used.


Frequently, however, we’ll want the system to ask us at runtime what we would

like to filter on. Instead of always filtering the view on the year 2019, we can specify

which year’s data we want to see. If we query the view from a reporting tool, we

even expect the reporting tool to pop up a dialog box and ask us what we would

like to filter on. In other words, you only specify the filter when the query is run on

the calculation view.

This dynamic type of filtering is done with variables. We define variables in the

Semantics node of a calculation view. In Chapter 6, we discussed the first two tabs

available in the Semantics node. Now we’ll look at one of the remaining two tabs:

Parameters.

In Figure 7.12, we see the Semantics node. In the Parameters tab, we select the + icon

to reveal the dropdown menu, and select the Variable option.

Figure 7.12 Creating a Variable in the Semantics Node

In the General work area (Figure 7.13), we start by specifying the Name of the vari-

able.

In the Selection Type field, we can choose what type of variable we want to create.

There are three types of variables:

� Single Value

The variable only asks for one (single) value; for example, the year is 2019.

� Interval

The variable asks for from and to values, for example, the years from 2015 to

2018.

� Range

The variable asks for a single value but combines it with another operator; for

example, the year is greater and equal to 2015.


Figure 7.13 Creating a Variable

When the Multiple Entries checkbox is enabled, we can, for example, select the

years 2013, 2016, and 2020 from the Value Help dialog.

By default, the variable uses the view or table that it’s based on to display a list of

available values in the Value Help dialog. Changing the View/Table Value Help

option allows us to specify a different view or table for the list of available values

in the Value Help dialog.

Ideally, the data source we specify should be in sync with the view we’re using the

variable on; otherwise, the users might choose a value from the Value Help dialog

that isn’t available in the current view and, therefore, won’t be shown any infor-

mation. The reason we do this is to ensure that we have consistent value help lists


for all our different views by specifying the same value help data source for all the

views. Sometimes this can also help to speed up the display of the value help.

With the Hierarchy option, we can link a hierarchy to our variable. Users can then

drill down using this hierarchy when they want to select a value from the Value

Help dialog. You’ll learn how to set up hierarchies later in this chapter.

We can specify default values for our variable in the Default Value(s) section. If the

user selects Cancel or presses (Esc) in the Value Help dialog, as shown later in this

chapter in Figure 7.18 and Figure 7.24, the variable will then use these default val-

ues.

We can specify the Attribute(s) on which we want to create our variable in the

Apply Filter section. This is the field on which we want to implement our “dynamic

filter.”

After we’ve created the variable, it will show up in the Columns tab of the Seman-

tics node, next to the name of the attribute we selected. You can see an example in

Figure 7.14.

Figure 7.14 Variable Shown in the Columns Tab of the Semantics Node


When we preview our calculation view, a Variable/Input Parameters screen will

appear in SAP Web IDE for SAP HANA, as shown in Figure 7.15. We have to supply

these values before SAP will run our view and return the results. If we supplied

default values, these values will be prepopulated in the fields, and we just continue

running the query on our calculation view.

When we select the icon on the right side of the FROM field (see Figure 7.15), a Value

Help dialog will appear with a list of available values that we can choose from.

Figure 7.15 Variable Dialog

Input Parameters

Input parameters are very similar to variables except where variables are specifi-

cally created for dynamic filtering, input parameters are more generic. For exam-

ple, we can use them in formulas or expressions.

Let’s look at an example: An outstanding payments report asks you for how many

months outstanding you want to see the results. This time period can then be used

in a calculated column’s expression to calculate the results we asked for. The

report can be used by the debt-collecting department for payments outstanding

longer than two months, while the legal department will use the same report to

ask for payments outstanding longer than six months.

We’ll start by learning how to create input parameters, and then take a quick look

at how to map them to different information-modeling objects.

Creating Input Parameters

Input parameters are created in the Semantics node (see Figure 7.16). We can also

create input parameters in all the node types. The input parameter work area and

the various dropdown menu options are shown in Figure 7.16.


Figure 7.16 Creating an Input Parameter

We start with the Name of the input parameter. The Parameter Type specifies what

type of data we expect to use this input parameter for. There are five types of input

parameters we can create:

� Direct

End users directly type in the value. We can specify the Data Type of the

expected value as Currency, Unit of measure, or Date.

� Column

End users can choose a value from all the possible values in a specified column

in the current view.

� Static List

End users can choose a value from a predefined (static) list of values.

� Derived From Table

End users can choose a value from data in another table.


� Derived From Procedure/Scalar Function

End users can choose a value from a list of values generated by a scalar function

or a procedure. Note that it must be a scalar function or a procedure that returns

a single list of values—basically a single column. A table function or a procedure

that returns table result sets (with multiple fields) isn’t allowed here. Input

parameters ask for input at runtime, which means that any generated values

can be overwritten by the end user when the query is executed.

The Default Values can be entered as a Constant value or an Expression. Similar to

variables, if the user selects Cancel or presses (Esc) in the Value Help dialog, the

input parameter will use this default value.

In Figure 7.16, we created an input parameter called DEMO_INPUT_PARAM. We can now

use this input parameter, for example, in a calculated column. When we use the

input parameter in an expression, we add a $$ at the start and end of the name of

the input parameter. This makes it very clear that we’re referring to an input

parameter. The name of our input parameter will become $$DEMO_INPUT_PARAM$$.

In Figure 7.17, we use the input parameter in the expression of a calculated column.

The following expression calculates the number of days between the date that the

end user supplies and the delivery date:

daysbetween(“DELIVERYDATE”, $$DEMO_INPUT_PARAM$$)

In this case, the input parameter has a Parameter Type of Direct and a Data Type of

Date. (We changed the Currency shown in Figure 7.16 to Date.)

Figure 7.17 Using an Input Parameter in an Expression for a Calculated Column


Tip

We have $$CLIENT$$ and $$LANGUAGE$$ built into SAP HANA. These don’t ask for values at

runtime, but instead give the SAP client number filter currently in use and the current

logon language of the end user.

You can see $$CLIENT$$ used later in this chapter in Figure 7.23.

When we preview the calculation view in SAP Web IDE for SAP HANA, and the

input parameter has a Data Type of Date, a calendar is displayed where you can

choose the date you want (see Figure 7.18).

Figure 7.18 Date Input Parameter

Mapping Input Parameters

Using input parameters is a very powerful modeling concept. We can link input

parameters to many different information-modeling objects. In Figure 7.19, we

select the Parameters tab, and then click on the Manage input parameter mappings

icon.

We can change the Type of mapping to the following:

� Data Sources

We can map the input parameters in this calculation view, for example, to the

input parameters in an underlying calculation view, such as a dimension view.


� Procedures/Scalar function for input parameters

We can map the input parameters in this calculation view to the input parame-

ters of a scalar function or procedure. Similarly to when we discussed the type

of input parameter when creating one, it must be a scalar function or a proce-

dure that returns a single list of values—basically a single “column.”

� Views for value help for variables/input parameters

We can map the input parameters in this calculation view to the input parame-

ters of an external view for value help.

� Views for value help for attributes

When you’re filtering the attributes in the underlying data sources that use

other calculation views as value help, you can map the input parameters in

these value help views to the input parameters of this calculation view.

Figure 7.19 Mapping Input Parameters

In Figure 7.19, we use a scalar function as the source. But the Source Input Parame-

ters list on the bottom left is empty because we have the Type of mapping specified

as Data Sources and not as Procedures/Scalar function for input parameters. If you

change to the correct mapping Type, the Source Input Parameters will automati-

cally show up.


Note

Unfortunately, the phrase input parameters has two meanings in SAP HANA. The first

meaning is for the input parameters we discussed earlier in this chapter, which ask for

“input” from a user at runtime. The second meaning is that SQLScript procedures and

functions can have input parameters and output parameters. These functions need

“input” and give “output.” The preceding mapping gives a way to link these different

types of input parameters.

Session Variables

Input parameters have a lot of uses as we’ve just seen. There are a few cases where

predefined input parameters can be very helpful. In some applications, you might

want to know extra information such as the name of the user that is logged in, the

name of the application that is calling the calculation view, and even the name of

the SAP HANA extended application services, advanced model (SAP HANA XSA)

user when a technical user is used to talk to the SAP HANA information models.

In many modern applications, such as the microservices running in SAP HANA

XSA containers, developers are using command-line parameters. These are values

that are passed to an application at execution time. These developers have been

asking for the same feature for SAP HANA applications. Users currently have to set

the value of an input parameter each time they call a calculation view that asks

them for a value. What we need is something that is set once and stays active for

the duration of each user’s session.

SPS 04

SAP HANA 2.0 SPS 04 has two more options in the dropdown menu when you add a new

parameter. The updated menu is shown in Figure 7.20.

Figure 7.20 Creating a Session Variable in SAP HANA 2.0 SPS 04


You can create a Fuzzy Search Parameter for helping users when searching text fields.

We’ll look at this option in Chapter 10.

The other option is to create a Session Variable. As the name suggests, this is like an

input parameter but with a value that is set for your current session. Think of working

session on the system as the time from when you log in until you log out.

You can use some predefined session variables, such as the name of the user that is

logged in. You can also create your own session variables, which allows you to control the

processing flow based on the value in your session variable.

For example, when working with financial data in a cube, you have both budget data and

actual expenses. (We discussed this in Chapter 5 when looking at how to build an infor-

mation model.) You can now set a session variable to only work with the budget data or

only work with the actual expenses. In a Union node, you can use the session variable in

the Mapping tab for constant pruning, or you can use a filter on the session variable in

another type of node.

Working with either hot or warm data in your session is another example. (We discussed

hot and warm data in Chapter 3 in the Data Aging section.)

Currency

SAP is well known for its business systems. An important part of financial and

business information models is their ability to work with currencies.

In Figure 7.21, we can see the Columns tab of a calculation view. We have a few out-

put fields that are currency fields, for example, four Sales amounts for different

regions.

When we look at the Properties of these fields, they are treated as numeric fields

(measures). We can improve on that by changing the Semantic Type property to

Amount with Currency Code.

When we change the Semantic Type of a field to currency, an Assign Semantics

screen appears as shown in Figure 7.22.

SPS 04

SAP HANA 2.0 SPS 04 also has a Hyperlink semantic type. Tools, such as web browsers,

that know how to consume hyperlinks can use this information for added functionality.


Figure 7.21 Assigning an Output Field to the Semantic Type “Amount with Currency Code”

Figure 7.22 Currency Conversion

Most of this screen is focused on currency conversion. Via the Internet, even small

companies can do business internationally, and companies can buy parts from


different suppliers worldwide with international currencies. Their taxes and finan-

cial reporting, however, happen in their home country in the local currency. We

therefore need to convert currencies as part of doing business.

When converting from one currency to another, there is a source currency and a

target currency. The date is equally important because exchange rates fluctuate all

the time. But what date do we use? Do we use the date of the quote, the date of the

invoice, the date of the delivery, the date the customer paid, the end of that month,

or the financial year-end? All of the mentioned dates are valid, so the answer

depends on the business requirements.

By default, most of the options in the Assign Semantics screen shown in Figure 7.22

and Figure 7.23 are unavailable. Enabling the Conversion flag opens up both the

Conversion Tables and Definition sections.

In SAP systems, the exchange rate data gets stored in some tables prefixed with

the TCUR name. There are many programs from banks and other financial institu-

tions that help update the TCUR tables in SAP systems. These TCUR tables are used by

the SAP business systems for currency conversions. SAP HANA also knows how to

use the TCUR tables. You can easily get a copy of these tables by installing the SHINE

package. It will create these TCUR tables for you to demonstrate currency conver-

sions in its information models. These tables are specified in the Conversion Tables

section.

Let’s go through some of the options in Figure 7.23 to learn more about currency

fields and currency conversions in SAP HANA. The top section has the following

options:

� Display Currency

You can specify the current field’s currency by selecting a specific currency

(Fixed) or selecting a Column that stores the currency for each record in the

transactions table.

� Decimal Shifts

Some currencies require more than two decimal places, which is indicated with

this flag.

� Shift Back

ABAP programs automatically do a decimal shift. When these ABAP programs

work with SAP HANA information views, they might receive currency data from

SAP HANA that has already been shifted, and a double decimal shift occurs. In

those cases, we need to set this flag to “undo” the extra Decimal Shift.


� Reverse Look Up

If you want to convert EUR to USD, but the system only has an entry for USD to

EUR, then the reverse lookup method will find a rate (USD to EUR), invert it, and

use this to convert EUR to USD.

Figure 7.23 Currency Conversion and the Definition Tab

Some of the Definition section options in the screen include the following:

� Source Currency and Target Currency

The specific currency codes, for example, EUR for euros and USD for US dollars,

can be specified directly in these fields. For the Source Currency we also have the

option of Column, and for Target Currency we have the following two additional

options:

– Column: If we’re storing multiple currencies in the same column, we’ll have

another column that specifies what currency each record is in. We can tell

SAP HANA to read the currency codes from that column and automatically

convert each of the individual records.


– Input Parameter: We can choose the currency code to use for the currency

conversion at runtime using an input parameter. We can get a financial

report to ask end users if they want the report in euros or dollars, or allow

them to pick any currency from the list of available currency codes in the

Value Help dialog.

We have to specify the input parameter as a VARCHAR data type with a length of

5 (refer to Figure 7.16 for an example).

� Conversion Date

The date of the conversion is used to read the exchange rate information for

that date from the TCUR tables.

� Generate

The converted currency value is shown in a new additional output column with

the specified name.

Some of the remaining options specify what type of currency conversion you want

to retrieve from the TCUR tables, for example, whether you’re using the buying rate

or the selling rate.

Finally, you can specify what SAP HANA should do in the case of errors. The Upon

Failure field allows three values:

� Fail

In this case, the currency conversion process will fail.

� Ignore

The currency conversion doesn’t do a conversion and puts the source amount

in the target column. This can be quite dangerous as a few million in one cur-

rency could be worth just a few dollars. The large (or small) amounts are copied

across as-is.

� Set to NULL

The target column’s value is set to NULL.

Tip

Note in the Client field how we can use the built-in $$client$$ input parameter.

When you run currency conversion and you’ve specified an input parameter for

the Source Currency or the Target Currency, the process will show you a Value Help

dialog as shown in Figure 7.24.


Figure 7.24 Value Help for the Source or Target Currency Field

Hierarchies

Hierarchies are important modeling artifacts that are often used in reporting to

enable intelligent drilldown. In Figure 7.25, we can see an example in Excel. Users

can start with a list of sales for different countries. They can then drill down to the

sales of the provinces (regions or states) of a specific country and, finally, down to

the sales of a city.

We’ve discussed the concepts of hierarchies in Chapter 5 already. We discussed two

types of hierarchies—level hierarchies and parent-child hierarchies—when we

should use each type of hierarchy, and what the differences are between these two

types of hierarchies.

Note

Two new types of hierarchies, spantree and temporal, were added in SAP HANA 2.0 SPS

02. We’ll quickly mention these new hierarchy types in the next section on hierarchy

functions with SQL.


Figure 7.25 Hierarchy of Sales Data from SAP HANA in Excel

You can use multidimensional expression (MDX) queries to use these hierarchies.

Just like SQL has become a standard for querying relational databases, MDX is a

standard for querying online analytical processing (OLAP) data. SAP uses Business

Intelligence Consumer Services (BICS) instead of MDX when communicating

between SAP systems. BICS is a proprietary connection, so SAP can only use it for

SAP systems. We use it because it’s a lot faster than MDX.

As hierarchies are important for analytics and reporting tools, we’ll look at the var-

ious aspects of hierarchies, including creating them, working with calendar-based

and time-based hierarchies, using them, and calling them from SQL.

Creating a Hierarchy

We create a hierarchy in the third tab of the Semantics node in a calculation view,

as shown in Figure 7.26. Select the + icon in the top-right menu on the Hierarchies

tab. You can choose to create a Level Hierarchy or a Parent Child Hierarchy from the

dropdown menu.

Figure 7.26 Creating a Hierarchy


You can specify the hierarchy options by clicking on the name of the hierarchy,

and completing the fields in the work area in SAP Web IDE for SAP HANA. You start

by specifying the Name of the hierarchy.

In Figure 7.27, we create a level hierarchy in SAP HANA where we add the different

levels. On each level, you select a specific field name in the dropdown list. Each

level uses a different column in a level hierarchy. You can also specify the sorting

order and on which column it has to sort.

Figure 7.27 Level Hierarchy

Figure 7.27 shows an example of a level hierarchy using geographic attributes.

Level 1 is the region (e.g., Africa or Asia), level 2 is the country, and level 3 is the city.

In Figure 7.28, you can see an example of a parent-child hierarchy of an HR organi-

zational structure with managers and employees. In this case, the manager has the

parent role, and the employee has the child role.


We can also use parent-child hierarchies for cost and profit centers or for a bill of

materials (BOM).

Figure 7.28 Parent-Child Hierarchy

We see that the parent-child hierarchy in Figure 7.28 has five sections at the bot-

tom that are collapsed at the moment. Clicking on these section headers opens

them up to provide more options.

In Figure 7.29, we see the first section for Properties of the hierarchy. The most-

used option here is defining what we should do with Orphan Nodes. This applies to

both level hierarchies (the last dropdown field in Figure 7.27) and parent-child hier-

archies (the second dropdown field in Figure 7.29).

Orphan nodes are employees that haven’t yet been assigned a manager, for exam-

ple, interns in some companies. You can specify in the dropdown menu that they

should become Root Nodes (e.g., board members in a company), generate an Error,

be Ignored, or be given a Step Parent.


In the case of Step Parent, you can specify a default node that these “orphans”

should report to, for example, to the HR manager. The Step Parent node must exist

in the hierarchy at the root level. The root level is the highest level in the company,

so it would be like saying the HR manager must be a part of the board. The field

next to this dropdown will become available, and you can enter the node ID, for

example, for the HR manager there.

The Aggregate All Nodes property is interpreted by the SAP HANA MDX engine to

specify that the values of intermediate nodes of the hierarchy should be aggre-

gated to the total value of the hierarchy’s root node. For better performance, this

should not be selected if there is no data posted on aggregate nodes.

A limitation of MDX is that it doesn’t support multiple parents and compound

parent-child definitions.

Figure 7.29 Properties Section of a Parent-Child Hierarchy

In Figure 7.30, you see the Additional Attributes section of a parent-child hierarchy.

Here you can specify additional attributes that are applicable to your hierarchy. In

this example, we added the first and last names of the people in the company.

Figure 7.30 Additional Attributes Section of a Parent-Child Hierarchy


The third section, Order By, allows you to specify how you want to group the result

set by one or more columns. In Figure 7.31, we split the result set up per company.

Figure 7.31 Order By Section of a Parent-Child Hierarchy

The fourth section is for Time Dependency, as discussed next.

Time-Dependent Hierarchies

Time-dependent hierarchies change over time, for example, before and after a

restructuring of the HR organizational structure, and are dependent on the time

period of your query.

The fields in this section are grayed out until we enable the Enable Time Depen-

dency option (Figure 7.32). After the rest of the fields become available, we supply

the required Valid From Column and Valid To Column fields. You can choose to

specify the Interval of the supplied dates using the From Date Parameter and To

Date Parameter, or you can choose a Key Date parameter, which you derive from

an input parameter.

Figure 7.32 Time-Dependency for Parent-Child Hierarchies


Note

Time-dependent hierarchies are only available for parent-child hierarchies in calculation

views.

Hierarchies in Value Help

As shown earlier in Figure 7.13, we can use hierarchies in variables to drill down

into the list of available values in a Value Help dialog.

In this case of level hierarchies, the variable’s reference column is pointing to the

deepest (leaf) level of the hierarchy. For example, in the earlier Figure 7.27, the vari-

able has to be created at the city level. It won’t work if you point it at the region or

country levels.

For parent-child hierarchies, you have to ensure that the variable’s reference col-

umn is pointing to the parent attribute of the hierarchy. Value help hierarchies can

also be time-dependent hierarchies.

Enabling Hierarchies for SQL Access

We can use hierarchies in business intelligence (BI) tools. When we use hierarchies

in these analytics and reporting tools, we can drill down into and sort the data

according to the hierarchy structures.

Something that was still missing in our discussion was how to enable hierarchies

for use in SQL, where developers can also use them. We can enable hierarchies for

SQL access in the case of shared hierarchies in a star join view.

You first create dimension views with hierarchies. Then you consume these

dimension views in a star join view. When you look at the Hierarchies tab in the

star join view in Figure 7.33, you’ll notice that there are two areas. The first is for

Local hierarchies, which are the hierarchies created in the star join view itself. The

second is Shared hierarchies, which shows the hierarchies inherited from the

dimension views.

In Figure 7.33, we have two shared hierarchies. When you select one of the shared

hierarchies, you see the hierarchy’s properties on the next screen. At the bottom,

you’ll find a new SQL Access area. When you expand this area, you can select the

Enable SQL Access checkbox. Two new fields become available with automatically

generated names. The Enable SQL Access checkbox is specifically for enabling this

shared hierarchy for SQL access.


Figure 7.33 Enabling SQL Access for Shared Hierarchies

You can also select the Enable Hierarchies for SQL access option in the View Proper-

ties tab of the Semantics node. In that case, it enables all the shared hierarchies for

SQL access.

Let’s look at the Node Column field. With this field, SAP HANA generates a new out-

put field. (We’ve seen other generated output fields in this chapter already, such as

calculated columns and restricted columns.) You can now use this new output

field in your SQL statements, for example, in a GROUP BY clause or for filtering your

result set based on values in your hierarchy. An example is shown in Figure 7.34.

(We discuss SQL and SQLScript in the next chapter.)

We also show how the node column appears as an output column in the Data Pre-

view of the star join view in SAP Web IDE for SAP HANA.


Figure 7.34 Using the Generated Node Column of the Hierarchy for SQL Access

Note

The Node Column field is only available for SQL queries, so it won’t show up if the view is

consumed by other graphical information tools.

Hierarchy Functions

In the previous section, we created hierarchies for use by reporting tools. In Figure

7.25, we started with a hierarchy in Excel. The hierarchies were all created in the

topmost semantic node. We get the benefit of these hierarchies when we use the


entire calculation view in BI tools with features such as drilldown and aggrega-

tions. In star join cubes, it can help us with dimensional navigation on fact tables,

including a time dimension. And we can use these hierarchies with Value Help

popups when choosing a value for a variable.

But what if we want to work with hierarchal data inside our calculation view? What

if we need to use the hierarchical metadata in analytical operations? For example,

some companies have a policy that people in a managerial position should have at

least seven people reporting to them. If they have less than seven people in their

team, they should also have some team tasks in addition to their management

responsibilities. How do we identify such individuals? We want a query that says,

“Show all managers where the number of direct team members is fewer than

seven.” In technical terms, we would ask, “Show all individuals with fewer than

seven hierarchy siblings at a hierarchy distance of one.”

This isn’t possible with the level and parent-child hierarchies we’ve created in the

semantic node over the past few years because these are used for navigation or

aggregation.

Note

The hierarchies we create in the Semantic node are mostly for use by analytics and

reporting tools outside of SAP HANA. The hierarchies we create in the Hierarchy Functionnodes are for use inside the calculation view.

As we saw in Chapter 6, SAP HANA has some new types of graphical calculation

view nodes that were added since SAP HANA 2.0 SPS 03. The one we’ll use here is

the Hierarchy Function node.

These nodes automatically generate hierarchy metadata from the data that is sup-

plied by the data sources. This generated metadata can be used inside the other

nodes in the graphical calculation view or in the output of the calculation view.

We can use the Hierarchy Function nodes in two ways in the graphical calculation

views:

� Generating the basic hierarchy metadata

We add a data source to the Hierarchy Function node. This node will then auto-

matically add additional output fields with values that help describe the basic

hierarchy, for example, how many people are reporting to a specific manager.

� Navigating the hierarchy

We feed the results (output) from the previous (generating) Hierarchy Function

Node into another Hierarchy Function Node. In this case, we select the navigate


features in the node, and it will automatically add additional fields with meta-

data for things such as the hierarchy depth. We can feed the output of the “gen-

erate” Hierarchy Function node directly into the “navigate” Hierarchy Function

node in the same calculation view, or we can first create a “generate” Hierarchy

Function node in a separate calculation view, and then feed this calculation view

into the “navigate” Hierarchy Function node. The latter approach is better, as

this allows us to create a library of hierarchy generator calculation views that we

can reuse in our information modeling.

We can also use the hierarchy functions in SQL and SQLScript.

We’ll now discuss the various ways that we just mentioned to use hierarchy func-

tions in more detail.

Data Used by Hierarchy Functions

Let’s take a practical look at how to work with hierarchy functions.

Note

Hierarchy function nodes will only work with data that has a parent-child hierarchy struc-

ture, for example, an organizational structure of a company.

In Listing 7.1, we’ve created a simple table. We have an employee name, the ID of

the employee, and the ID of the employee’s manager. We save this as an .hdbcds

file and then build the file to get SAP HANA to create the table.

namespace hier.funcs;context orgstruct {

entity HR_ORG {key EMPLOYEE_ID: hana.VARCHAR(12) not null;EMPLOYEE_NAME: hana.VARCHAR(100);MANAGER_ID: hana.VARCHAR(12);

};};

Listing 7.1 Creating a Table for Sample Data Using Core Data Services

In this example, we’ll use a small subset of an organizational structure, as shown in

the Figure 7.35. When we go to the SAP HANA Database Explorer screen in SAP Web

IDE for SAP HANA, right-click on the table, and select Data Preview, we see the

screen as shown in Figure 7.36. By selecting the + icon on the toolbar, it allows us to

add new records. We added the organizational structure shown into the table,

showing the parent-child relationships in this data.


Figure 7.35 Subset of an Organizational Structure Illustrated Graphically

Figure 7.36 Subset of an Organizational Structure as Table Data

We’re now ready to use this table as a data source in a Hierarchy Function node in a

calculation view.

Generate Hierarchy Functions

On the left side of Figure 7.37, you see a dimension calculation view with a Hierar-

chy Function node. You can add one data source to this node. On the right side of

Figure 7.37, you can see the definition of the node.

The default type of Hierarchy Function node is for generating the hierarchy meta-

data. The top two menus in Figure 7.37 show the options for Generation and Navi-

gation.

You have to specify which column in your source data (table) is the parent and

which column is the child. In this case, the MANAGER_ID is the parent, and the

Rudi H

Andrea Hans-Joachim

Pascal Hans-Heinrich Martin Rudi Phillip Skhu Tshepo

Vasanth

Vidya

Kevin Roman


EMPLOYEE_ID is the child. In the Start field, you can specify which department you

want to analyze. All other records that aren’t part of that department will be

ignored. In this case, the records numbered 15–17 in Figure 7.36 won’t be included

in the result set.

You can additionally specify the Depth of the hierarchy you want. If you leave this

blank, it will go down to the lowest levels. You can also specify how orphan nodes

should be handled (Orphan Handling). Finally, you can specify if you want the

result set sorted; for example, you can sort the hierarchy by the names of the peo-

ple by selecting the EMPLOYEE_NAME field in the Order By area.

Figure 7.37 Creating a Dimension Calculation View with a Generation Hierarchy Function Node

In Figure 7.38, all three columns of the table are mapped to the output of the Hier-

archy Function node. Notice that the mapping automatically renamed the Man-

ager_ID field to PARENT_ID and the Employee_ID field to NODE_ID.

If you look at the number of output columns, however, you’ll notice that there are

nine columns. The generation Hierarchy Function node automatically created an

additional six columns.


Figure 7.38 Mapping the Columns in a Hierarchy Function Node and Showing the Additional Auto-Generated Columns

The six auto-generated columns are as follows:

� Hierarchy Rank

This shows the position of the record in the generated hierarchy. When you

specify an Order By field, this could influence these values.

� Hierarchy Tree Size

The number of records (nodes) below this node in the hierarchy. It includes this

node in the number.

� Hierarchy Parent Rank

The position of its parent node.

� Hierarchy Level

The level (depth) in the hierarchy of this node.

� Hierarchy is Cycle

Whether this node is part of a cycle. This happens when a node is both a parent

and a child in the same path. The value can be 0 or 1.


� Hierarchy is Orphan

Whether this node is an orphan. The value can be 0 or 1.

In Figure 7.39, you can see the output from the generation Hierarchy Function

node. For each record or node in the hierarchy, you can see the various values in

the auto-generated fields just described.

Figure 7.39 Previewing the Data of a Hierarchy Function Node

You can now use this dimensional calculation view with the generation Hierarchy

Function node in other calculation views.

Navigate Hierarchy Functions

Figure 7.40 shows a new dimensional calculation view. It’s very similar to the one

shown in Figure 7.37, except that the Hierarchy Function node doesn’t have a table

as a data source; instead it uses the previous calculation view with the generation

Hierarchy Function node. In this case, you specify the Hierarchy Function node as a

navigation type.

In Figure 7.37, you can see how to specify the navigation as moving down the hier-

archy (Hierarchy Descendants), moving up the hierarchy (Hierarchy Ancestors), or

staying at the same level (Hierarchy Siblings).

When looking at the Columns tab, you’ll see that a single auto-generated field

called Hierarchy Distance was created. This gives the distance between this node

and another node, expressed by the difference in levels. You can specify the other

node by filling in the Start Rank field.


Figure 7.40 Creating a Dimension Calculation View with a Navigational Hierarchy Function Node

Figure 7.41 illustrates one way of achieving this. Here we moved the data source to

a project node below the navigation Hierarchy Function node, and then we inserted

another project node with a filter between them. The filter is a simple expression

that allows you to filter the result set to a single record. In this case, we specified a

unique NODE_ID.

In the navigation Hierarchy Function node, you can select any of the fields from the

filtered Projection node. In this example, it’s HIERARCHY_RANK.

When you now look at the Mapping and Columns tabs, you’ll notice an additional

auto-generated column called START_RANK, as shown in Figure 7.42.

We started this discussion by giving an example of identifying all managers where

the number of direct team members is fewer than seven. In technical terms, we

would ask, “Show all individuals with fewer than seven hierarchy siblings at a hier-

archy distance of one.”

You’re now ready to build such queries in your graphical calculation views.


Figure 7.41 Adding a Filter to a Navigation Hierarchy Function Node

Figure 7.42 Showing the Additional Auto-Generated Column in a Navigation Hierarchy Function Node


Hierarchy Functions with SQL

These hierarchy functions have been available in SQLScript for a while already. In

fact, more hierarchy functions are available in SQLScript than the ones we dis-

cussed here. These are described in the SAP HANA Hierarchy Developer Guide at

http://s-prs.co/v487620. You can call them, for example, using table functions. This

is described in the next chapter.

Following are examples of some available hierarchy functions:

� Hierarchy span tree

This function generates a minimal tree to prove connectivity for each node

when you just need to know if a node is connected, how many times, and the

possible paths. You can use this for checking authorizations that use something

like an organizational structure where you just need to establish that there is at

least one connection to the root. This then indicates that permission is granted.

Basically, a span tree includes only the first shortest path to any node. If there

are other longer paths, they are removed. A good use case for this is to find the

flights with the lowest number of connections. You don’t want to fly via two

stopovers when you can get a direct flight.

� Hierarchy temporal

This function generates a hierarchy that presents time-based relationships

between the nodes. This can, for example, be used when one person stands in

for someone else during a vacation period. The relations are only valid for a lim-

ited time.

� Hierarchy leveled

This function works with data that represents a level hierarchy.

� Hierarchy composite ID

This function combines columns in the source data to create nodes, for exam-

ple, combining first and last names for each employee.

� Hierarchy descendants aggregation and hierarchy ancestors aggregation

This function is similar to the standard descendants and ancestors functions,

but it also provides optimized aggregation.

SPS 04

Hierarchy span tree and hierarchy temporal functions are now also available in the Hier-archy Function node for graphical calculation views.




In this chapter, we touched on different ways to enhance our SAP HANA calcula-

tion views and enrich our information models. The following list describes the

important terms covered in this chapter:

� Calculated columns

Adds additional output columns to our calculation views and allows us to calcu-

late new values.

� Counters

Shows how many times something has occurred within a result set.

� Restricted columns

Restricts the data shown in a particular column or field and outputs the

restricted values in an additional output field.

� Filter expressions

Used if we don’t want to show all the data from a specific table or to only show

specific data. This can use both static and dynamic filtering.

� Domain fixed values

Values from the ABAP Data Dictionary, and the users can only choose from the

available list when they enter data into the fields.

� Variables

The three types of variables:

– Single value: The variable only asks for one (single) value; for example, the

year is 2019.

– Interval: The variable asks for from and to values, for example, the years from

2016 to 2019.

– Range: The variable asks a single value but combines it with another opera-

tor; for example, the year is greater and equal to 2018.

� Input parameters

The five types of input parameters we can create:

– Direct: Directly enter the value. We can specify the Data Type of the expected

value. There are three data types we can use for Direct: Currency, Unit of mea-

sure, and Date.

– Column: Choose a value from all the possible values in a specified column in

the current view.

– Derived From Table: Choose a value from data in another table.


– Static List: Choose a value from a predefined (static) list of values.

– Derived From Procedures/Scalar Function: Choose a value from a list of values

generated by a scalar function or procedure.

� Currency conversion

Used when converting from source currency to target currency. A date for the

conversion also has to be supplied.

� Hierarchies

Used in reporting to enable intelligent drilldown.

� Time-dependent hierarchies

Hierarchies that change over time, for example, before and after a restructuring

of the HR organizational structure, and are dependent on the time period of

your query.

� Hierarchy function nodes

Nodes where hierarchies are generated from a data source. These hierarchies

can be used for analytical purposes inside the calculation view.

Table 7.1 shows in which view node some of the features are available. For example,

filter expressions aren’t available in Graph and Hierarchy Function nodes.

Node Type Calculated

Columns

Counters Restricted

Columns

Input

Parameters

Filter

Expressions

Join Yes – – Yes Yes

Star Join Yes Yes Yes Yes Yes

Projection Yes – – Yes Yes

Aggregation Yes Yes Yes Yes Yes

Rank – – – Yes Yes

Union Yes – – Yes Yes

Graph – – – Yes –

Intersect,

Minus

Yes – – Yes Yes

Hierarchy

Function

– – – Yes –

Table 7.1 Features per Node


Practice Questions









1. In which type of node can you create calculated columns? (There are 2 correct

answers.)

� A. Rank node

� B. Graph node

� C. Union node

� D. Projection node

2. You want to build a hierarchy on a time-based data structure, such as year,

month, and day. What type of hierarchy do you use?

� A. Parent-child hierarchy

� B. Level hierarchy

� C. Ragged hierarchy

� D. Unbalanced hierarchy

3. Which are features of a calculated column? (There are 2 correct answers.)

� A. It’s a column in the result set.

� B. Its calculations are persisted.

� C. It’s created in the output area.

� D. It’s created in the Semantics node.

4. You want to show a male and female option to end users. What feature do you

use? (There are 3 correct answers.)

� A. Domain fixed values

� B. Value help


� C. Variables

� D. Expressions

� E. Input parameters

5. You’re creating a currency conversion. What options do you have for the Tar-

get Currency field? (There are 3 correct answers.)

� A. Select a fixed currency.

� B. Select a column.

� C. Use the Derived from Table option.

� D. Use an input parameter.

� E. Select a table type.

6. Where do you create a variable?

� A. In the context menu of a workspace

� B. In the Semantics node

� C. In the output area of a Join node

� D. In the work area of an Aggregate node

7. You have logs from a website. How do you show the total number of website

visitors that used a mobile phone? (There are 2 correct answers.)

� A. Create a restricted column.

� B. Create a hierarchy expression parameter.

� C. Create a filter expression.

� D. Create a rank column.

8. What can you do with a level hierarchy?

� A. Make it time-dependent

� B. Use it for a bill of materials (BOM) structure

� C. Use it for drilldown in a value help list

� D. Show variable deepness in the data


9. What can a variable be used for?

� A. Currency conversion

� B. Calculated column expression

� C. Call a procedure in SQLScript

� D. Filtering

10. You’re working with a currency measure. What is decimal shift used for?

� A. To increase the accuracy of the currency conversion

� B. To undo the decimal adjustment for ABAP

� C. For currencies with more than two decimal places

� D. To avoid rounding errors during the conversion

11. What variable type do you use if you want to allow the user to choose the years

from 2016 to 2019?

� A. Single value

� B. Interval

� C. Range

� D. Multiple entries

12. What do you create to find the number of unique items sold per shop?

� A. A calculation view with a restricted column in an Aggregation node

� B. A calculation view with a counter in a Projection node

� C. A calculation view with a counter in an Aggregation node

� D. An analytic view with a calculated column in a Star Join node

13. What can you use to limit the number of records produced by a calculation

view? (There are 3 correct answers.)

� A. SAP client restrictions

� B. Domain fixed values

� C. Filter expressions

� D. Restricted columns

� E. Generated columns


14. You created a calculated column in a star join view for a table containing the

number of units ordered, the price per unit, and the number of units in stock.

You get the wrong results. What could be the problem?

� A. You mistyped the calculated column expression.

� B. You chose the SQL expression syntax.

� C. You did not enable the Keep Flag.

� D. You did not enable the currency conversion.

15. Where can you create a filter expression?

� A. In an Aggregation node

� B. In a Data Foundation node

� C. In a Projection node

� D. In the Semantics node

16. Which tables do you need for currency conversion?

� A. DD07*

� B. TCUR*

� C. BICS*

� D. SHINE*

17. What do you use to build a financial report for the company’s board members

that allows them to choose if they want the report to be in euros or dollars?

� A. An input parameter from an Aggregation node

� B. A variable from the Semantics node

� C. A variable from a Projection node

� D. An input parameter from the Semantics node

18. What type of input parameters do you use to choose the data from a field in

the current table?

� A. Direct

� B. Derived from table

� C. Value help

� D. Column


19. What are properties of domain fixed values? (There are 2 correct answers.)

� A. They are based on the ABAP Data Dictionary.

� B. They ask end users for input at runtime.

� C. They are static values.

� D. They are based on SQLScript functions.

20. Which generated output column uses hierarchies?

� A. Rank Column

� B. Node Column

� C. Restricted Column

� D. Calculated Column

21. Which hierarchies can you enable for SQL access?

� A. Level hierarchies in a calculation view of type cube

� B. Local hierarchies in a star join view

� C. Time-dependent hierarchies in a dimension view

� D. Shared hierarchies in a star join view

22. What do you require when creating a hierarchy for analysis inside a graphical

calculation view? (There are 2 correct answers.)

� A. Data for a level hierarchy

� B. Data for a parent-child hierarchy

� C. A Hierarchy Function node

� D. A table function

23. Which column gets automatically generated in a navigational Hierarchy Func-

tion node?

� A. Hierarchy Rank

� B. Hierarchy Parent Rank

� C. Hierarchy Distance

� D. Hierarchy Level



1. Correct answers: C, D

You can create calculated columns in a Union node and a Projection node.

If you got this wrong, it might be that you remember it from SAP HANA 1.0. Cal-

culated columns are now available in more view nodes than before.


You use a level hierarchy to build a hierarchy on a time-based data structure.

Time-based means you have year on level 1, months on level 2, and days on

level 3.

Only a parent-child hierarchy can be a time-dependent hierarchy. (The words

are confusing, so watch out. Here we mentioned the data structure.) It just illus-

trates that you could achieve fairly similar functionality with both types of

hierarchies, although the approaches are quite different.

Ragged and unbalanced hierarchies aren’t mentioned in SAP HANA.


Features of a calculated column: it’s a column in the result set and is created in

the output area.

Its calculations aren’t persisted! It’s NOT created in the Semantics nodes.

4. Correct answers: B, C, E

You can show a male and female option to users with either variables or input

parameters, and by opening the value help.

Domain fixed values can be used for filtering. Male and female got mentioned

in the “Domain Fixed Values” section, but the context there was completely dif-

ferent.


The options you have for the target currency field in a currency conversion are

as follows:

Select a fixed currency.

Select a column.

Use an input parameter.


You create a variable in the Semantics node.



You show the total number of website visitors that used a mobile phone by cre-

ating either a restricted column or a filter expression.

A hierarchy expression parameter is created when you enable a hierarchy for

SQL access.

A rank column will rank them by numbers but doesn’t show the total number.

(You could perhaps rank all the data, rank the data, and then search for the total

number in the rankings. So, technically, it’s possible but requires that you still

manually search the correct output.)


A level hierarchy can be used for drilldown in a value help list. Only parent-child

hierarchies have time-dependency, variable deepness in the data, and can be

used for a BOM structure.


A variable can be used for filtering.

You’ll use input parameters for currency conversion and in calculated column

expression.

You can’t call a procedure via a SQLScript variable in SQLScript (which we’ll look

at in the next chapter). You use the CALL syntax and use output parameters.


Decimal shift is used for currencies with more than two decimal places. Deci-

mal shift back is used to undo the decimal adjustment for ABAP.


You use an interval variable type if you want to allow the user to choose dates

from 2016 to 2019.

Range would allow for years > 2016. Don’t confuse the range type with the inter-

val type.


You create a calculation view with a counter in an Aggregation node to find the

number of unique items sold per shop. You need a counter for this, so two

answers are eliminated. A calculation view with a counter in a Projection node

isn’t valid because a counter can’t be created in a Projection node.

13. Correct answers: A, B, C

You can use SAP client restrictions, domain fixed values, and filter expressions

to limit the number of records produced by a calculation view.


Restricted columns don’t restrict the number of records. The word columns

indicates that it’s an additional output column. An additional output column

can’t reduce the number of records.


If you get errors in a calculated column in a star join view, you must enable the

Keep Flag and ensure that you don’t create the calculated column in the Aggre-

gation node.


You can only create a filter expression in a Projection node.


You need the TCUR* tables for currency conversion. You need the DD07* tables for

domain fix values.


You use an input parameter from the Semantics node (where it gets created) to

build a financial report for the company’s board members that allows them to

choose if they want the report to be in euros or dollars. You just reference the

already created input parameter in the currency conversion.


You use a Column type of input parameter to choose the data from a field in the

current table. The Direct type is used to directly type in values. The Derived From

Table type is used to get data from another table.

19. Correct answer: A, C

Domain fixed values are based on the ABAP Data Dictionary and are static val-

ues.


A node column is generated when you enable hierarchies for SQL access.


Shared hierarchies in a star join view can be enabled for SQL access.

22. Correct answer: B, C

You require both the data for a parent-child hierarchy and a Hierarchy Function

node when creating a hierarchy for analysis inside a graphical calculation view.

You can only use the data for a level hierarchy when calling additional hierar-

chy functions from SQLScript using a table function.

(Note that actually all four answers could be correct if you create a table

function where you use the data for a level hierarchy and call an additional


hierarchy function from SQLScript, and then use this table function inside a

graphical calculation view. You’ll understand this better after having read

the next chapter.)


The Hierarchy Distance column gets automatically created in a navigational

Hierarchy Function node.

The other three columns get automatically created in a generation Hierarchy

Function node.

Takeaway

You should now know how to enhance SAP HANA calculation views with the nine

different modeling features we’ve discussed in this chapter. You know how to cre-

ate calculated and restricted columns to add additional output fields to your calcu-

lation views. You’ve looked at filtering and filter expressions, and you’ve learned

how to limit the data early in your modeling process.

You can now identify when to create variables and input parameters to allow busi-

ness users to specify what outputs they want to see in the result sets of your calcu-

lation views at runtime.

Working with currency, you now understand how to add currency conversions to

your financial reports.

Finally, you now know how to create hierarchies to provide powerful drilldown

capabilities for your end users and for analyzing hierarchical data in your organi-

zation.

Summary

We learned how to work with calculated columns, restricted columns, and filters in

our calculation views. We added variables and input parameters to add a new level

of dynamic interaction to our modeling by allowing the end users to specify what

they are looking for in each query of the SAP HANA calculation views. We also saw

how to enrich our calculation views with currency conversions and hierarchies.

In the next chapter, we’ll discuss how to use SQL and SQLScript to provide the

advanced features that you might not be able to do with the graphical calculation

views we discussed in previous chapters.

Chapter 8

SQL and SQLScript


� Get a basic understanding of SQL and how to use it with your

SAP HANA information models

� Learn SQLScript in SAP HANA and find out how it extends and

complements SQL

� Understand how the SAP HANA modeling environment with

SQL and SQLScript has changed over the past few years

� Create and use table and scalar functions with your graphical

SAP HANA information views

� Develop procedures in SAP HANA using SQL and SQLScript

Chapter 8 SQL and SQLScript338

We’ve made a few references about SQL and SQLScript up to this point. Even in

Chapter 7, we enabled hierarchies for SQL access, and you saw how to use the gen-

erated node column in a GROUP BY clause. In this chapter, we look at how to use SQL

and SQLScript to enhance your graphical modeling capabilities.

You’ve already seen how you can use SQLScript in your calculation views, for

example, in the expression editors of the calculated and restricted columns, in

filter expressions, and in creating default values for input parameters (in the Cre-

ating Input Parameters section of Chapter 7). We’ll also see some use of it in Chap-

ter 13 when we look at security.

In many large SAP HANA projects, you’ll reach a point at which you need to do

something more than what is offered by the graphical information views. Infor-

mation modeling using SQL and SQLScript offer more powerful options. You lose

the productivity and simplicity of working graphically with information views,

but you gain flexibility and control.

As SAP HANA has matured, the graphical modeling tools have grown such that the

requirements for SQL and SQLScript have been reduced. In the past, after you

started working with SQL and SQLScript, you had to continue in that environment

(or choose to create any subsequent layers in your information models there as

well). Since SAP HANA 1.0 SPS 11, you can work graphically with your SAP HANA

information views, address the occasional constraint with SQL and SQLScript, and

bring this development back into the graphical SAP HANA modeling environ-

ment. You can then continue building the remaining layers of your information

models using the graphical information views. The focus has therefore shifted

toward the graphical modeling environment for SAP HANA modelers.

Note

There is definitely less focus now on SQL and SQLScript in the average modeling workflow

than there was in the past. However, a good grasp of development processes and basic

programming knowledge will always be to your benefit.

In this chapter, we’ll get you up to speed quickly on the SAP HANA information

views and how to integrate your SQL/SQLScript development back into the graph-

ical modeling environment.


Real-World Scenario

You’ve been working on a large SAP HANA project for a few months when a

group of business users asks if you can help to make security access more

powerful and sync it to automatically use their hierarchal organizational

structures. You base the analytic privileges on a procedure that easily

exploits these structures.

The company is quickly moving into the area of mobile business services and

plans to deliver some new, real-time business dashboards to executives and

managers. SAP HANA is an obvious choice, and your modeling and develop-

ment skills are required to build the link to the mobile screens. Using your

SQLScript knowledge, you quickly figure out ways to create powerful dash-

boards that respond fast on the mobile screens.

You’ve noticed lately how the expression editors in the graphical modeling

environment are now using SQL syntax. By leveraging your SQL knowledge,

you improve many of the features end users are using daily in their analytics.



knowledge of modeling using SQL and SQLScript.


topics:

� Standard SQL and how it relates to SAP HANA

� SQLScript in SAP HANA

� How to create and use user-defined functions (UDFs), especially table functions

� Procedures in SAP HANA using SQL and SQLScript

Note




Structured Query Language (SQL) is a widely used database programming language

standardized by the American National Standards Institute (ANSI) and the Interna-

tional Organization for Standardization (ISO). The standard is often referred to as

ANSI SQL.

Despite the existence of these standards, most SQL vendors extend ANSI SQL to

provide enhanced capabilities for their individual database products. Microsoft

uses an extended version called Transact-SQL (T-SQL), and Oracle uses PL/SQL.

SAP HANA provides SQLScript, which is an extension of ANSI SQL, to provide extra

capabilities for the unique in-memory components that SAP delivers. We’ve

already seen many of these features, for example, column tables, parameterized

information views, delta buffers, multiple result sets in parallel, and built-in cur-

rency conversions. The ANSI SQL standard doesn’t provide these features. How-

ever, SAP HANA supports ANSI SQL statements.

In this chapter, we provide only a short introduction to SQL and SQLScript, and we

do assume you have some knowledge of SQL. You can find the full SAP HANA SQL

and SQLScript reference guides at http://s-prs.co/v487620.

SQL

SQL uses a set-oriented approach for working with data.

Tip

Rather than working with individual records, you should focus on working with the entire

result set that consists of many data records.

You specify what should happen to all the data records in the result set at once.

This allows databases to be efficient when processing data because they can paral-

lelize the processing of these records. Databases are faster at processing data than

application servers because SQL is a set-oriented declarative language, whereas

application servers typically use procedural or object-oriented languages that pro-

cess records one at a time.

SQL Language

SQL is used to define, manipulate, and control database objects. These actions are

specified by various subsets of the language:



� Data Definition Language (DDL)

This is used, for example, to CREATE, ALTER (modify), RENAME, or DROP (delete) table

definitions.

� Data Manipulation Language (DML)

This is used to manipulate data, for example, insert new data into a table or

update the data in a table. Examples in SQL include INSERT, UPDATE, and DELETE.

� Data Query Language (DQL)

This is used to SELECT (access) data. It doesn’t modify any data.

� Data Control Language (DCL)

This is used for data security, for example, to GRANT privileges to a database user.

� Transaction Control Language (TCL)

This is used to control database transactions, for example, to COMMIT or ROLLBACK

database transactions.

SQL is also subdivided into several language elements, as listed in Table 8.1.

Element Short Description

Identifiers Names of database objects, for example, a table called BOOKS.

Data types Specifies the characteristics of the data stored in the database, for exam-

ple, a string stored as a NVARCHAR data type.

Operators Used to assign, compare, or calculate values, for example, =, >=, + (add), *

(multiply), AND, and UNION ALL.

Expressions Clauses that produce result sets consisting of either scalar values or tables

consisting of columns and rows of data. Examples include aggregations,

such as SUM, or IF…THEN…ELSE logic using CASE.

Predicates Specifies conditions that can be evaluated to TRUE, FALSE, or UNKNOWN. These

can be used to filter statement and query results or to change program

flow. They are typically used in the WHERE conditions of the SELECT state-

ment, for example, A >= 100.

Queries Retrieves data from the database based on specific criteria. Queries use

the SELECT statement, for example, SELECT * from BOOKS.

Statements Commands to create persistent effects on schemas or data and control

transactions, program flow, connections, and sessions.

Functions Provides methods to evaluate expressions. They can be used anywhere an

expression is allowed. Various functions are built into SQL, for example,

LENGTH(BOOKNAME).

Table 8.1 Elements of SQL


There are a few important points to remember about SQL when working with SAP

HANA:

� Identifiers can be delimited or undelimited, meaning the names of database

objects are or are not enclosed in double quotes. If you don’t enclose identifiers

with quotes, they are treated as uppercase. Therefore, BOOKS, Books, and books all

reference the same table name.

Delimited identifiers are treated as case-sensitive and can include characters in

the name that would be forbidden by undelimited identifiers. SAP HANA uses

Unicode, so the range of characters in a name can be wide. In this case, table

"BOOKS" and table "books" are treated as two separate tables.

� NULL is a special indicator used by SQL to show that there is no value.

� The LIKE predicate can be used for SQL searches. SELECT author FROM books WHERE

title LIKE ‘%HANA%’; will search for all books with the word HANA in the title.

We’ll look at this in more detail in Chapter 10.

� Statements in SQL include the semicolon (;) statement terminator. This is a

standard part of SQL grammar. Insignificant whitespace is generally ignored in

SQL statements, and queries and can be used to make such statements and que-

ries more readable.

We’ll now look at some SQL statements that correspond to SAP HANA modeling

constructs you’re already familiar with.

Creating Tables

The short version of the SQL syntax to create a table is as follows:

CREATE [<table_type>] TABLE <table_name>;<table_type> ::= COLUMN | ROW | GLOBAL TEMPORARY |GLOBAL TEMPORARY COLUMN | LOCAL TEMPORARY | LOCAL TEMPORARY COLUMN |VIRTUAL

This means that you have to type “CREATE TABLE BOOKS;” to create an empty

table called BOOKS. In this example, <tablename> is BOOKS. You can choose what to

type in place of the <tablename> placeholder.

Anything in square brackets, such as [<table_type>], is optional. If you want your

table to be a column table, type “CREATE COLUMN TABLE BOOKS;”, for example. If you

don’t specify the type of table, the system will create a row table because that’s

what ANSI SQL expects.


Note

The CREATE COLUMN TABLE is already the first bit of SQLScript that you meet. Standard ANSI

SQL just caters for CREATE TABLE. The extra COLUMN keyword is specific to SAP HANA, and

this “extension” is therefore part of SQLScript.

In addition, note that the syntax of SQLScript fits in exactly with that standard SQL syn-

tax. The same syntax is used to create a standard row table (CREATE TABLE) and an SAP

HANA column table (CREATE COLUMN TABLE).

With SAP HANA SPS 03, we got a new table type called SAP HANA versioned tables.

Also known as system-versioned tables, they have actually been part of ANSI SQL

since 2011. Instead of a single table, you use two tables. Both these tables must be

in the same SAP HANA Deployment Infrastructure (HDI) folder. The first table is a

normal column table. The second is a history table that is linked to the first table

with a WITH SYSTEM VERSIONING HISTORY TABLE clause. This table has the same fields as

the first table, with additional VALIDFROM and VALIDTO time stamp fields. The first

table contains your data, just like any other table. But when you update a record,

instead of forgetting the previous values, the system-versioned table automati-

cally copies the values in the updated record to the second table and updates the

VALIDFROM and VALIDTO fields, before updating the record in the first table.

When you query the first table, it behaves like a normal column table. However,

you can execute special SELECT statements with a “time-travel clause” on these

types of tables to get the data as it was at a certain time. We can therefore read a

table as it was at any point in time. This can be useful for tables that refer to finan-

cial transactions and can completely change the way that systems perform period

closings. Rather than you having to code all the logic to handle historical data, it’s

built in to the database engine. You also don’t have to put logic for reading histor-

ical data into every database query.

Tip

SAP HANA versioned tables replace the old HISTORY COLUMN tables. You should not use HIS-TORY COLUMN tables.

SAP Enterprise Architecture Designer (SAP EA Designer) (discussed in Chapter 4)

has a feature in which it creates SAP HANA versioned tables for you with just a few

clicks. You create a normal column table and then activate the Versioning option in

the properties.


SPS 04

SAP HANA SPS 04 adds a variation of SAP HANA versioned tables called application time

period tables. These are basically the same as SAP HANA versioned tables, but they use

valid time periods specified by the business rather than the system time of when a record

was updated. You can create these by creating a .hdbapplicationtime file. Just remember

to ensure that you also add the correct plugin in the (hidden) .hdiconfig file.

Reading Data

The query, the most common operation in SQL, uses the SELECT statement—for

example:

SELECT [TOP <unsigned_integer>] [ ALL | DISTINCT ]<select_list> <from_clause> [(<subquery>)] [<where_clause>][<group_by_clause>] [<having_clause>] [<order_by_clause>][<limit_clause>] [<for_update_clause>] [<time_travel_clause>];

One of the shortest statements that can be used to read data from table BOOKS is

SELECT * FROM BOOKS;. This returns all the fields and data from table BOOKS, in effect

putting the entire table in the result set.

Tip

We recommend never using SELECT * statements as it can severely impact the query per-

formance of column tables. Because SAP HANA primarily uses column tables, this type of

SELECT statement should be avoided!

A better way is to specify the <select_list>, for example, SELECT author, title,

price FROM BOOKS;. In this case, you only read the author, title, and price columns for

all the books.

Note the [<time_travel_clause>] for use with SAP HANA versioned tables.

SPS 04

What is tremendously helpful for developers in SAP HANA 2.0 SPS 04 is the FOR JSONclause. This returns the result set in JavaScript Object Notation (JSON) format.


Filtering Data

In SAP HANA, we sometimes use a filter, by using the WHERE clause as part of a

SELECT statement, for example:

SELECT author, title, price FROM BOOKS WHERE title LIKE ‘%HANA%’;

Creating Projections

Inside a Projection node in SAP HANA views, you can rename fields easily via the AS

keyword in SQL, for example:

SELECT author, title AS bookname, price FROM BOOKS;

The title field is now renamed to bookname in the result set.

Creating Calculated Columns

Creating calculated columns looks almost the same as renaming fields, but there is

an added formula, as shown here:

SELECT author, title, price * discount AS discount_price FROM BOOKS;

In this case, we added a new calculated column called discount_price.

Created Nested Reads and Aggregates

In SAP HANA views, we often have many layers; in SQL, we use nested SELECT state-

ments. These nested queries are also known as subqueries. For example:

SELECT author, title, price FROM BOOKSWHERE price > (SELECT AVG(price) FROM BOOKS);

The SELECT AVG(price) FROM BOOKS statement (subquery) returns the average price of

all the books in table BOOKS. The entire query above returns the books that are more

expensive than the average price.

You can create aggregates, such as with an aggregation node in a calculation view,

using standard aggregation functions in SQL. We discussed aggregation in Chapter 5.


Creating Joins

SAP HANA views normally have many joins. Here is an example of how to create

an inner join between two tables:

SELECT BOOKS.author, BOOKS.title, BOOKS.price,BOOK_SALES.total_sold FROM BOOKSINNER JOIN BOOK_SALESON BOOKS.book_number = BOOK_SALES.booknumber;

The field on which both tables are joined is the book_number field.

Creating Unions

Union nodes in calculation views can be performed by using the UNION operator, for

example:

SELECT author, title, price FROM BOOKSUNION

SELECT author, title, price FROM BOOKS_OUT_OF_PRINT;

You can also use the UNION ALL operator instead of UNION. The UNION operator returns

only distinct values, whereas UNION ALL will return all records, including duplicate

records.

Conditional Statements

You can handle if-then logic in SQL via the CASE statement, for example:

SELECT author, title, price,CASE WHEN title LIKE '%HANA'%' THEN 'Yes' ELSE 'No' ENDAS interested_in_book FROM BOOKS

Here, we’ve added a calculated column called interested_in_book to our query. If

the book’s title contains the word HANA, we indicate that we’re interested in it.

With that short introduction, you’ve seen how to do many of the things we’ve

done before using graphical modeling in calculation views, but now using SQL.


SQLScript

SQLScript exposes many of the memory features of SAP HANA to SQL developers.

Column tables, parameterized information views, delta buffers, multiple result

sets in parallel, built-in currency conversions at the database level, fuzzy text

searching, spatial data types, and Predictive Analysis Libraries (PAL) make SAP

HANA unique. The only way to make this functionality available via SQL queries,

even from other applications, is to provide it via SQL extensions.

We’ve come across many of these features in earlier chapters. We’ll look in more

detail at the fuzzy text search and PAL in Chapter 10, as well as learn more about

the Application Function Library (AFL) that includes predictive and business func-

tions that can be used with SQLScript. In Chapter 11, we’ll learn more about spatial

functions, graph processing, and series data in SAP HANA

In Chapter 5, we saw the spatial join type used between a ST_POINT data type (e.g., a

location) and an ST_POLYGON (e.g., a suburb). The spatial functions can be called from

SQLScript to access and manipulate spatial data.

SAP HANA also has to cater to SAP-specific requirements, for example, limiting

data to a specific “client” (e.g., 100) in the SAP business systems. In SAP HANA, you

can use a SESSION_CONTEXT(CLIENT) function in the WHERE clause of a query to limit

data in a session to a specific client.

In this section, we’ll look at the various features of SQLScript.

Separate Statements Using Variables

One of the unique approaches of SQLScript is the use of variables to break a large,

complex SQL statement into smaller, simpler statements. This makes the code

much easier to read and to understand, and it also helps with SAP HANA’s perfor-

mance because many of these smaller statements can be run in parallel.

Let’s look at an example:

books_per_publisher = SELECT publisher, COUNT (*) ASnum_books FROM BOOKS GROUP BY publisher;

publisher_with_most_books = SELECT * FROM:books_per_publisher WHERE num_books >=(SELECT MAX (num_books) FROM :books_per_publisher);


We would normally write this as a single SQL statement using a temporary table or

by repeating a subquery multiple times. In our example, we’ve broken this into

two smaller SQL statements by using table variables.

The first statement calculates the number of books each publisher has and stores

the entire result set into the table variable called books_per_publisher. This variable

containing the entire result set is used twice in the second statement.

Tip

Notice that the table variable is prepended in SQLScript with a colon (:) to indicate that

this is used as an input variable. All output variables use only the name, and all input vari-

ables have a colon prepended.

The second statement uses :books_per_publisher as input and uses a nested SELECT

statement.

The SQLScript compiler and optimizer will determine how to best execute these

statements, whether by using a common subquery expression with database hints

or by combining them into a single complex query. The code becomes simpler to

write, easier to understand, and more maintainable.

SPS 04

In SAP HANA 2.0 SPS 04, these variables can even be treated like tables. You can modify

data in SQLScript table variables with statements such as INSERT, UPDATE, and DELETE. You

must just remember to prepend a colon to the variable name, for example, INSERT INTO:variable . . ..

By breaking the SQL statement into smaller statements, we also mirror the way

we’ve learned to build graphical information views in SAP HANA. Look at the two

calculation views in Figure 8.1; if you had to write all this in SQL, it would be quite

difficult to create optimal code.

Just like we start building graphical information views from the bottom up, we do

the same with our SQLScript code. We won’t write all the SQLScript code here, but

just some pseudocode to illustrate the point. We start with a Union node on the

right side of the model in Figure 8.1:

union_output = SELECT * FROM AMER UNION SELECT * FROM EMEA;


The first nodes in the left-side model are Join nodes. You can use the output from

a previous Join node as the source for one side of the join:

join_output = SELECT * FROM :previous_join_outputINNER JOIN SELECT * FROM get_employees_by_name_filter('Jones');

Each time, the output from the lower node becomes the input for the next higher

node. As you can see, this layered approach to information modeling is easy to

write in SQLScript.

Figure 8.1 SAP HANA Calculation Views in Layers


Imperative Logic

Most of the SQL and SQLScript code we’ve worked with to this point has used

declarative logic. In other words, we’ve used the set-oriented paradigm that con-

tributes to SQL’s performance. We’ve asked the database to hand us back all the

results immediately when the query has completed. Occasionally, you might pre-

fer to receive the answers to a query one record at a time. This is most often the

case when running SQL queries from application servers. In these cases, you’ll use

imperative logic, meaning you’ll “loop” through the results one at a time. You can

also use conditionals, such as in if-then logic. As such, the imperative login con-

trols the flow of data to the application server.

Imperative logic is (mostly) not available in ANSI SQL; therefore, SAP HANA pro-

vides it as part of SQLScript.

Tip

Remember that imperative logic doesn’t perform as well as declarative logic. SAP HANA

can’t optimize the imperative logic to the same performance levels as that of declarative

logic. If you require the best performance, try to avoid imperative logic.

There are different ways to implement imperative logic in SQLScript:

� WHILE loop

The WHILE loop will execute some SQLScript statements as long as the condition

of the loop evaluates to TRUE:

WHILE <condition> DOsome SQLScript statements…

END WHILE

� FOR loop

The FOR loop will iterate a number of times, beginning with a loop counter start-

ing value and incrementing the counter by one each time, until the counter is

greater than the end value:

FOR <loop-var> IN [REVERSE] <start_value> .. <end_value> DOsome SQLScript statements…

END FOR

SPS 04

In addition to declarative and imperative logic, we also get recursive SQLScript in SAP

HANA 2.0 SPS 04. This is where a function or procedure can call itself. We can illustrate

this by using the standard example of recursively calculating factorials, as shown in Lis-

ting 8.1.


PROCEDURE factorial(in depth int, out exitvalue int)BEGIN

if :depth < 1 thenexitvalue = 1;

elsecall factorial(:depth-1, exitvalue);exitvalue = :depth * :exitvalue;

end ifEND

Listing 8.1 Recursive SQLScript in SAP HANA 2.0 SPS 04

The recursion depth for the procedure can be set from 8 to 128, with a default of 32. Func-

tions don’t have an explicit depth limit, but you’ll get a runtime error if you run out of

memory. Library procedures and functions don’t support recursion.

Dynamic SQL and Security

Another powerful feature in SQLScript is the ability to create and execute dynamic

SQL. Let’s illustrate this via an example. Figure 8.2 shows a web form asking for the

user’s mobile telephone number at the top of the figure. We insert this mobile

number into a partially prepared SQL statement and then execute this SQL state-

ment using the EXEC statement.

Figure 8.2 Dynamic SQL with Values from a Web Form

Mobile number: +86 139-1234-5678

“SELECT col1, col2, col2 FROM employees WHERE mobile_num =‘“ & mobile_number & “’;”

EXEC “SELECT col1, col2, col2 FROM employees WHEREmobile_num =‘+86 139-1234-5678’;” �


Each user gets a unique SQL statement created especially for him or her. The per-

formance isn’t as good as static SQL statements, but this is more powerful and flex-

ible.

There is one problem: if you’re not careful, hackers can use this setup to break into

your system. This has been one of the top security vulnerabilities in web applica-

tions for many years now.

Let’s look at the web form again. This time, in Figure 8.3, hackers enter more infor-

mation than just the mobile telephone number. They add a closing quote and a

semicolon. When this is inserted into our prepared SQL statement, it closes the

current statement. (Remember that SQL statements are terminated by a semico-

lon.)

Figure 8.3 Dynamic SQL with SQL Injection from a Web Form

Anything after this is executed as another SQL statement—so the hackers can

enter something like DROP DATABASE that might delete the entire database. Or they

can try various SQL statements to read or modify data values in the database.

SAP HANA includes some new features related to this problem, such as the new

IS_SQL_INJECTION_SAFE function, which helps prevent such attacks.

If you remember to prevent security vulnerabilities such as SQL injection,

dynamic SQL can enable you to write some powerful applications. You can change

Mobile number: +86 139-1234-5678’;DROP DATABASE;‘

“SELECT col1, col2, col2 FROM employees WHERE mobile_num =‘“& mobile_number & “’;”

EXEC “SELECT col1, col2, col2 FROM employees WHEREmobile_num =‘+86 139-1234-5678’;DROP DATABASE;’’” �


data sources at any time, link them to applications, and build adaptive user inter-

faces.

Again, keep in mind that this power also comes at the price of some performance.

Because SAP HANA has to evaluate SQL statements at runtime, the performance of

dynamic SQL statements can’t be as good as that of static SQL statements.

The other downside of using dynamic SQL statements is that the result set can’t be

passed to a SQLScript variable because the EXEC statement doesn’t return an entire

result set. (You can return a single record into a scalar variable.)

User-Defined (Table and Scalar) Functions

User-defined functions (UDFs) are custom, read-only functions in SAP HANA. You

write your own functions that can be called in a similar fashion as SQL functions.

There are two types of UDFs: scalar functions and table functions. The main differ-

ence is whether you want a function to return values as simple data types (e.g., an

integer) or in complete “table-like” result sets. A scalar function can return one or

more values, but these values have to be single data types. A table function returns

a set of data.

Tip

Both types of UDFs are read-only. You may not execute any DDL or DML statements

inside these functions.

We create functions as shown in Listing 8.2.

CREATE FUNCTION <func_name> [(<parameter_clause>)]RETURNS <return_type>[LANGUAGE SQLSCRIPT][SQL SECURITY <DEFINER | INVOKER>][DEFAULT SCHEMA <default_schema_name>] AS

{ BEGIN<function_body>

END }

Listing 8.2 Creating User-Defined Functions

Here are a few important points to note about UDFs:

� RETURNS

Depending on the type of data we return, RETURNS will be a scalar or a table func-

tion.


� LANGUAGE

UDFs can only be written in SQLScript.

� SQL SECURITY

This indicates against which user’s security privileges this function will be exe-

cuted:

– If we choose the DEFINER option, this function will be executed by the definer

of the function, meaning the technical user of the container.

– If we choose the INVOKER option, this function will be executed by the end

user calling the function, and all security privileges will be limited to that

user’s assigned authorizations.

You can create UDFs in the SAP HANA system. We discussed in Chapter 4 how to

create new files.

We create .hdbfunction objects for scalar and table functions. Figure 8.4 shows a

newly created function in SAP Web IDE for SAP HANA.

Figure 8.4 Newly Created (Blank) Table Function in SAP Web IDE for SAP HANA

In the following two sections, we’ll take a look at examples of both scalar functions

and table functions.

Scalar Functions

Scalar UDFs support primitive SQL types as input. Let’s walk through an example

to better understand their usage.

Start by creating a scalar function as in Listing 8.3 that takes the price of a book and

a discount percentage as the two inputs and creates two output values. The first

output value is the price of the book after the discount is applied, and the second

is the discount amount.


FUNCTION apply_discount (book_price decimal(15,2),book_discount decimal(15,2))

RETURNS discounted_price decimal(15,2), discount decimal(15,2),LANGUAGE SQLSCRIPTSQL SECURITY INVOKER AS

BEGINdiscounted_price := :book_price – ( :book_price * :book_discount);discount = :book_price * :book_discount;

END;

Listing 8.3 Scalar Function That Returns Two Values

You can call the scalar function with the following code:

SELECT apply_discount (59.50, 0.1).discounted_price asyour_book_price, apply_discount (59.50, 0.1).discountas discount_amount from DUMMY;

In this case, you get two values back: 53.55 and 5.95. Note the DUMMY table; it doesn’t

really get called, so, in this case, it allows you to call the scalar function.

Figure 8.5 shows a similar example of a scalar function from the SAP HANA Inter-

active Education (SHINE) demo content, as shown in SAP Web IDE for SAP HANA.

Figure 8.5 Example of Scalar Function Found in SHINE Content


A scalar function can be called in an SQL statement as you would call a field. You

can also call it as part of the WHERE clause, but you can’t use it as the data source (in

the FROM clause.)

Table Functions

Table UDFs support table types as an input and returns a table as output. Let’s walk

through an example to better under their usage.

Listing 8.4 shows a modified version of a table function found in the SHINE con-

tent. This code performs an inner join between an employee table and an address

table to find the employee’s email address. The neat trick here is in the WHERE

clause: SAP HANA performs a fuzzy text search on the employee’s last name. You’ll

still get an email address, even if you mistyped the person’s last name. This is dis-

cussed in more detail in Chapter 10.

FUNCTION get_employees_by_name_filter" (lastNameFilter NVARCHAR(40))RETURNS table (EMPLOYEEID NVARCHAR(10),

"NAME.FIRST" NVARCHAR(40),"NAME.LAST" NVARCHAR(40),EMAILADDR NVARCHAR(255))

LANGUAGE SQLSCRIPTSQL SECURITY INVOKER AS

BEGINRETURN

SELECT a.EMPLOYEEID, a."NAME.FIRST", a."NAME.LAST", b.EMAILADDR,FROM Employees AS a INNER JOIN Addresses AS bON a.EMPLOYEEID = b. EMPLOYEEIDWHERE contains("NAME.LAST", :lastNameFilter, FUZZY(0.9));

END;

Listing 8.4 Table Function That Performs a Fuzzy Text Search on Last Names to Find Email Addresses

Figure 8.6 shows the table function from the SHINE demo content in SAP Web IDE

for SAP HANA. We can use the table function from Figure 8.6 as a data source in a

graphical calculation view. In an SQL statement, you can place a table function in

the FROM clause.

An example is shown in Figure 8.7, in which this table function is used in a join

with the result set from a Union node. (This isn’t really a good example—we should

really join on the key field—but is used here purely to illustrate the point.)


Figure 8.6 Example of Table Function Found in SHINE Content

Figure 8.7 Using a Table Function in a Graphical Calculation View


Procedures

A procedure is a subroutine consisting of SQL statements. Procedures can be called

or invoked by applications that access the database. Procedures are sometimes

also known as stored procedures.

Procedures are more powerful than UDFs. Whereas UDFs are read-only and can’t

change any data in the system, procedures can insert, change, and delete data. You

can execute any DDL or DML statements inside a procedure. Whereas a table func-

tion can only return one result set, procedures can return many result sets as out-

put. These multiple result sets can be scalar and tabular.

Listing 8.5 illustrates how to create procedures.

CREATE PROCEDURE <proc_name> [(<parameter_clause>)][LANGUAGE <SQLSCRIPT | R>][SQL SECURITY <DEFINER | INVOKER>][DEFAULT SCHEMA <default_schema_name>][READS SQL DATA [WITH RESULTS VIEW <view_name>]] AS

{ BEGIN [SEQUENTIAL EXECUTION]<procedure_body>

END }

Listing 8.5 Creating Procedures

The following syntax items are used in Listing 8.5:

� LANGUAGE

Procedures can be written in SQLScript or in R. R is a popular open-source pro-

gramming language and data analysis software environment for statistical

computing.

� SQL SECURITY

Indicates against which user’s security privileges this function will be executed:

– If we choose the DEFINER option, this function will be executed by the definer

of the function, meaning the technical user of the container. This is the

default for procedures.

– If we choose the INVOKER option, this function will be executed by the end

user calling the function, and all security privileges will be limited to that

user’s assigned authorizations.

� READS SQL DATA

Marks the procedure as being read-only. The procedure then can’t change the

data or database object, and it can’t contain DDL or DML statements. If you try


to activate a read-only procedure that contains DDL or DML statements, you’ll

get activation errors.

� WITH RESULTS VIEW

Specifies that a view (that we have to supply) can be used as the output of a read-

only procedure. When a result view is defined for a procedure, it can be called by

an SQL statement in the same way as a table or view.

� SEQUENTIAL EXECUTION

Forces sequential execution of the procedure logic; no parallelism is allowed.

� PARALLEL EXECUTION

Allows parallel execution of the procedure logic.

Some important points to note with regards to procedures:

� A procedure doesn’t require any input and output parameters. If we have input

and output parameters, they must be explicitly typed. The input and output

parameters of a procedure can have any of the primitive SQL types or a table

type. In SQLScript, we can also define table types. Table types are similar to data-

base tables but don’t have an instance. They are like a template for future use.

They are used to define parameters for a procedure that needs to use tabular

results.

� If we require table outputs from a procedure, we have to use table types.

� Any read-only procedure may only call other read-only procedures. The advan-

tage of using a read-only procedure is that certain optimizations are available

for read-only procedures, such as not having to check for database locks; this is

one of the performance enhancements in SAP HANA.

� Note that WITH RESULTS VIEW is a subclause of the READS SQL clause, so the same

restrictions apply to it as those for read-only procedures.

Let’s switch gears. Similar to UDFs, we can create procedures. In this case, we create

an .hdbprocedure object. Figure 8.8 shows a procedure in SAP Web IDE for SAP

HANA. Procedures are called with the CALL statement. You can call the procedure

shown in Figure 8.8 with the following statement:

CALL "get_product_sales_price" ('HT-1000', ?);

The results of this CALL statement, as executed in the SQL Console in SAP Web IDE

for SAP HANA, are shown in Figure 8.9.


Figure 8.8 Example Procedure Found in SHINE Content

Figure 8.9 Results of Calling a Procedure Directly from the SQL Console


You can call procedures with multiple result sets, which read and write transac-

tional data from an SAP HANA extended application services, advanced model

(SAP HANA XSA) application using server-side JavaScript such as Node.js. The REST-

style web services created will use an HTTP GET for reading data and an HTTP PUT

for writing data.

You’ll notice that how we use procedures is quite different from how we use UDFs.

We can, however, use procedures in a similar way as functions if we specify the

read-only and result view clauses.

In Listing 8.6, we create a procedure that uses a pre-created result view called

View4BookProc and a table type called BOOKS_TABLE_TYPE.

CREATE PROCEDURE ProcWithResultView(IN book_num VARCHAR(13),OUT output1 BOOKS_TABLE_TYPE)LANGUAGE SQLSCRIPTREADS SQL DATA WITH RESULT VIEW View4BookProc AS

BEGINoutput1 = SELECT author, title, price FROM BOOKS WHERE

isbn_num = :book_num;END;

Listing 8.6 Read-Only Procedure Using a Result View

We can now call this procedure from a SQL statement, just like a table function:

SELECT * FROM View4BookProc (PLACEHOLDER."$$book_num$$"=>'978-1-4932-1230-9');

Another way of calling a procedure is from a calculation view. In Chapter 7, we used

input parameter mapping. We can also map the input parameter in a calculation

view to the input parameter of a procedure.

SQLScript Libraries

SQLScript built-in libraries were introduced with SAP HANA 2.0 SPS 02. The num-

ber of built-in libraries is growing continuously. With SPS 03, user-defined librar-

ies (UDLs) were introduced, where you can create your own library functions and

procedures. These library members have to be wrapped with an SQLScript artifact

like a procedure, function, or anonymous block. They can only be used for variable

assignment, and they can’t be used in an SQL query.


SPS 04

The preceding restrictions have been removed with SPS 04. You now don’t need to wrap

library members in an SQLScript artifact, and you can use library functions directly in an

SQL query.

SPS 04 also adds a library for logging, as well as an end user unit test framework.

Multilevel Aggregation

Sometimes, you may need to return multiple result sets with a single SQL state-

ment. In normal SQL, this might not always be possible, but we can use multilevel

aggregation with grouping sets to achieve this in SQLScript.

Let’s start by looking at an example (Listing 8.7) in SQLScript of how we would cre-

ate two result sets.

-- Create result set 1books_pub_title_year = SELECT publisher, name, year, SUM(price)FROM BOOKS WHERE publisher = pubisher_idGROUP BY publisher, name, year;-- Create result set 2books_year = SELECT year, SUM(price) FROM BOOKSGROUP BY year;

Listing 8.7 Creating Two Result Sets in SQLScript

You can create both of these result sets from a single SQL statement using GROUPINGSETS, as shown in Listing 8.8.

SELECT publisher, name, year, SUM(price)FROM BOOKS WHERE publisher = pubisher_idGROUP BY GROUPING SETS ((publisher, name, year), (year))

Listing 8.8 Creating Multiple Result Sets from a Single SQL Statement with Grouping Sets

You now have a good understanding of SQLScript and the powerful capabilities it

brings to your modeling toolbox. Let’s now look at when you should use this func-

tionality and how to ensure that you use the latest variations of these features.

Views, Functions, and Procedures

Taking a step back, let’s look at how to best address our modeling requirements. In

SAP HANA we have three main approaches: views, UDFs, and procedures. Let’s first

discuss each of these options, and then we’ll discuss when you should use each

one.


Information Views

We’ve discussed SAP HANA information views in detail in preceding chapters.

You’ve built these views using the graphical environment, and you’ve now also

learned how to also create these view using SQLScript.

Some of the characteristics of views include the following:

� Views are treated like tables. You can use SELECT, WHERE, GROUP BY, and ORDER BY

statements on views.

� Views are static. They always return the same columns, and you look at a set of

data in the same way.

� Views always return one result set.

� Views are mostly used for reading data.

User-Defined Functions

After learning about UDFs, you should be familiar with their characteristics, such

as the following:

� Functions always return results. A table function returns a set, and a scalar func-

tion returns one or more values.

� Functions require input parameters.

� The code in UDFs is read-only. Functions can’t change, update, or delete data or

database objects. You can’t insert data with functions, so you can’t use transac-

tions or COMMIT and ROLLBACK in functions.

� Functions can’t call EXEC statements; otherwise, you could build a dynamic

statement to change data and execute it.

� You can call the results of functions in SELECT statements and in WHERE and HAV-

ING clauses.

� Functions can be used in a join.

� Table functions can’t call procedures because procedures can change data. You

have to stick to the read-only paradigm of functions.

� Functions use SQLScript code. This can include imperative logic in which you

loop through results or apply an if-then logic flow to the results.

Procedures

Procedures are the most powerful of the three approaches. Some of the character-

istics of procedures include the following:


� Procedures can use transactions with COMMIT and ROLLBACK. They can modify

tables and data, unless they are marked as read-only. Read-only procedures may

only call other read-only procedures.

� Procedures can call UDFs, but UDFs can’t call procedures.

� Procedures don’t have to return any values but may return multiple result sets.

Procedures also don’t need to specify any input parameters.

� Procedures provide full control because all code functionality is allowed in pro-

cedures.

� Procedures can use try-catch blocks for errors.

� Procedures can also use dynamic SQL—but be aware of security vulnerabilities

such as SQL injection when using dynamic SQL.

SPS 04

Up to SAP HANA 2.0 SPS 04, dynamic SQL was always considered as read-write. With SPS

04, you can now specify dynamic SQL in a procedure to be read-only by using the READSSQL DATA clause. This especially helps performance by parallelizing multiple read-only

dynamic SQL statements.

If you use dynamic SQL in a read-only procedure, SAP HANA automatically assumes that

the dynamic SQL in such a procedure is also read-only. Why else would you include it into

a read-only procedure?

When to Use What Approach

After all that detail, the recommendation of when to use each of the three

approaches is quite easy to make:

� In order of functionality, procedures come in first, followed by UDFs, and then

views.

� In order of performance, we have the reverse: views give the best performance,

then UDFs (because they are permanently read-only), and then procedures.

Tip

Use the minimal approach to get the results you require. If you can do it with views, then

use views. If you can’t do it with views, then try using UDFs. If that still doesn’t meet the

requirements, create a procedure. This way, you do the least work and get the best per-

formance.


Catching Up with SAP HANA 2.0

Figure 8.7 illustrated how to use a table function in a graphical calculation view.

This option changes how we approach modeling in SAP HANA projects and shows

how strong the graphical modeling environment has become.

We can work graphically with our SAP HANA views, create UDFs or procedures for

the occasional constraint, and bring these back into the graphical SAP HANA mod-

eling environment. We can then continue building the remainder of our SAP

HANA information models using the graphical information views.

We should, however, stick to the latest modeling best practices:

� The main guideline to keep in mind is that you should create design-time

objects that can be deployed to multiple systems, such as development, testing,

and production. Don’t create runtime objects as these can’t easily be copied to

other systems.

� Many modelers and developers used the SQL Console to create procedures. This

creates runtime objects that can’t be transported to the production system. The

recommended way is to create design-time objects—for example, .hdbproce-

dure objects—instead of using the CREATE PROCEDURE statement.

� Don’t create database objects (e.g., tables) using SQL statements such as CREATE

TABLE. Rather use core data services (CDS) objects such as .hdbcds. Even the

.hdbtable is deprecated. You can use .hdbtabledata for loading data into the

tables created by the CDS objects from data files (e.g., comma-separated values

[CSV] files).


The following important terminology was used in this chapter:

� SQL

Structured Query Language (SQL) is a database programming language used to

enhance your modeling process when you have requirements that graphical

views can’t fulfill.

� SQLScript

SQLScript exposes many of the memory features of SAP HANA to SQL develop-

ers. This includes working with column tables, parameterized information

views, delta buffers, multiple result sets in parallel, built-in currency conver-

sions at the database level, fuzzy text searching, spatial data types, and PAL. The


only way to make this functionality available via SQL queries, even from other

applications, is to provide it via SQL extensions, that is, SQLScript.

� User-defined functions (UDF)

UDFs are custom, read-only functions in SAP HANA.

� Procedures (stored procedures)

A procedure is a subroutine consisting of SQL statements. Procedures can be

called or invoked by applications that access the database.

� Imperative logic

Occasionally, you might prefer to receive the answers to a query one record at a

time. This is most often the case when running SQL queries from application

servers. In these cases, you’ll use imperative logic, meaning you’ll “loop”

through the results one at a time

� Dynamic SQL

Dynamic SQL is when you build up SQL strings on the fly using code and then

execute them.

Practice Questions









1. What paradigm describes the SQL language?

� A. Set-oriented

� B. Object-oriented

� C. Procedural

� D. Application-focused


2. To what subset of SQL does the SELECT statement belong?

� A. DML

� B. DDL

� C. DCL

� D. DQL

3. True or False: Delimited identifiers are treated as case-sensitive.

� A. True

� B. False

4. What is used in SQL to indicate that there is no value?

� A. Zero

� B. Empty quotes

� C. NULL

� D. Spaces

5. What character is used to terminate SQL statements?

� A. ? (question mark)

� B. : (colon)

� C. ; (semicolon)

� D. . (period)

6. What SELECT statement isn’t recommended for use with column tables?

� A. SELECT *

� B. SELECT with a time-travel clause

� C. SELECT ALL

� D. SELECT in a subquery

7. True or False: A union returns all values in a result set, even duplicate records.

� A. True

� B. False


8. What character is prepended to a SQLScript variable when it’s used as an input

variable?

� A. ! (exclamation mark)

� B. : (colon)

� C. _ (underscore)

� D. & (ampersand)

9. What should you keep in mind when using imperative logic in SQLScript?

� A. It delivers the best possible performance.

� B. You can only use if-then logic.

� C. You can loop through records.

� D. It matches SQL’s set-oriented paradigm.

10. What should you keep in mind when using dynamic SQL? (There are 2 correct

answers.)

� A. It’s always bad for security.

� B. It could be used for SQL injection.

� C. You can dynamically change your data sources.

� D. It delivers the best possible performance.

11. You created a user-defined function with a return type of BIGINT. What type of

function have you created?

� A. SQL function

� B. Scalar function

� C. Table function

� D. Window function

12. In which programming languages can you create procedures in SAP HANA?


� A. SQLScript

� B. L

� C. JavaScript

� D. R


13. You created a user-defined function with the INVOKER security option. Which

user’s authorizations are checked when the function is executed?

� A. The owner of the function

� B. The container’s technical user

� C. The SYSTEM user

� D. The user calling the function

14. What is the preferred way to create a procedure?

� A. Use the CREATE PROCEDURE statement in the SQL console.

� B. Create a .procedure file.

� C. Create a .hdbprocedure file.

� D. Use the Import menu in SAP Web IDE for SAP HANA.

15. With which statement can you call a table function? (There are 2 correct

answers.)

� A. CALL

� B. INSERT INTO

� C. SELECT

� D. EXEC

16. What statement is allowed in a user-defined function?

� A. COMMIT

� B. EXEC

� C. INSERT

� D. JOIN

17. You want to call a procedure in a SELECT statement (e.g., a table function).

Which clauses do you have to specify? (There are 2 correct answers.)

� A. LANGUAGE SQLSCRIPT

� B. WITH RESULTS VIEW

� C. SEQUENTIAL EXECUTION

� D. READS SQL DATA


18. What SQL construct do you use to return multiple result sets from a single SQL

statement?

� A. GROUPING SETS

� B. SELECT subquery

� C. JOIN

� D. UNION

19. What can be used to return multiple result sets?

� A. Scalar function

� B. Table function

� C. Procedure

� D. View

20. What should you keep in mind about user-defined functions?

� A. They can use imperative logic.

� B. They can commit transactions.

� C. They can use dynamic SQL.

� D. They can call procedures.

21. What should you keep in mind about procedures? (There are 3 correct

answers.)

� A. They can call views.

� B. They are NOT open to SQL injection.

� C. They must return values.

� D. They may have input parameters.

� E. They can call table functions.

22. Where should you use SQLScript outside of the development environment

discussed in this chapter?

� A. Calculation views

� B. Security

� C. Text analytics


� D. Creating tables

� E. Reporting tools

23. True or False: You should create tables with .hdbtable CDS objects.

� A. True

� B. False

24. What does SQLScript provide in SAP HANA?

� A. Variables to break up complex statements

� B. Powerful client-side processing

� C. Flow control logic

� D. Creation of column tables

� E. Aggregation functions, such as AVG()



SQL uses a set-oriented paradigm. Object-oriented and procedural paradigms

are used for application-level programming languages. SQL is database-focused

rather than application-focused. (Imperative logic is more application-focused.)


The SELECT statement belongs to the Data Query Language (DQL) subset of SQL.

Data Manipulation Language (DML) refers to SQL statements such as INSERT and

UPDATE. Data Definition Language (DDL) refers to creating tables and so on. Data

Control Language (DCL) refers to security.


True. Yes, delimited identifiers are treated as case-sensitive. Undelimited (with-

out quotes) identifiers are treated as if they were written in uppercase.


NULL is used in SQL to indicate that there is no value.


A semicolon (;) is used to terminate SQL statements.



A SELECT * statement isn’t recommended for use with column tables. Column

tables are stored per column, and SELECT * forces the database to read every col-

umn. It becomes much faster when it can ignore entire columns.


False. A UNION ALL statement returns all values in a result set, even duplicate

records. UNION only returns the unique records.


A colon (:) is prepended to a SQLScript variable when it’s used as an input vari-

able.


You can loop through records using imperative logic in SQLScript. It does not

deliver the best possible performance. Declarative logic (the set-oriented para-

digm) is much faster. The word only makes the “You can only use if-then logic”

statement false because we can also use loop logic with imperative logic.


Dynamic SQL can be used for SQL injection, and you can dynamically change

your data sources.

The word always makes the “It’s always bad for security” statement false.

Dynamic SQL doesn’t deliver the best possible performance.


You create a scalar function when it has a return type of BIGINT. This returns a

single value, not a table-like result set.

SQL functions and window functions aren’t UDFs.


You can create procedures in SQLScript and R in SAP HANA.


A user-defined function with the INVOKER security option checks the authoriza-

tions of the user calling the function when this function is executed.

The technical user of the container is used if the DEFINER security option is used.

The owner of the function is no longer applicable after the function has been

deployed.

The SYSTEM user is never used in this context.



The preferred way to create a procedure is to create an .hdbprocedure file.

Methods A and B are deprecated! (This shows how much things have changed

over the past few years.)


You can call a table function with SELECT and EXEC statements. EXEC can be used

to build a dynamic SQL statement that contains a SELECT statement, for exam-

ple.

You CAN’T use a function to INSERT INTO because it’s read-only.

CALL is how procedures are called.


JOIN is allowed in a UDF.

COMMIT, EXEC, and INSERT aren’t read-only statements. A UDF won’t even build

when you have these in the function.


You need the WITH RESULTS VIEW and READS SQL DATA clauses to call a procedure in

a SELECT statement.


GROUPING SETS can return multiple result sets from a single SQL statement.

All the other statements use multiple sources but return a single result set.


Procedures can return multiple result sets.

Table functions and views can only return single result sets.

Scalar functions return (multiple) single values, not sets.


UDFs can use imperative logic.

They can’t commit transactions or use dynamic SQL (because they are read-

only), and they can’t call procedures. (However, procedures can call UDFs.)

21. Correct answers: A, D, E

Procedures can call views and table functions, and they may have input param-

eters.

They may, but not must, return values.

Because they can use dynamic SQL, they could be open to SQL injection.


22. Correct answers: A, B, C

You use SQLScript in calculation views (e.g., in filter expressions, and in calcu-

lated and restricted columns), in security, and in text analytics.

You shouldn’t use SQLScript to create tables. That way, they’ll be runtime

objects and can’t easily be deployed to multiple systems. Create them using

CDS as design-time objects.

Reporting tools don’t use SQLScript.


No, you should NOT create tables with old .hdbtable CDS objects.

You should create them with .hdbcds CDS objects.

24. Correct answers: A, C, D

SQLScript provides variables to break up complex statements, flow control

logic, and creation of column tables

Takeaway

You now have new tools and approaches in your modeling toolbox. We started by

providing a basic understanding of SQL and how to use it with your SAP HANA

information models. You learned about SQLScript in SAP HANA and discovered

how it extends and complements SQL, especially by creating and using UDFs and

procedures to use with your graphical SAP HANA information views.

Summary

You should now be up to date with the SQL and SQLScript working environments

and know how to use them in your SAP HANA information modeling.

We’ve now covered the modeling topics that taught you to build very powerful

information models. The next chapter focuses on how to load data into SAP HANA.

Chapter 9

Data Provisioning


� Learn what data provisioning is

� Understand the basic data provisioning concepts

� Get to know some of the various SAP data provisioning tools

available for SAP HANA

� Know when to use the different SAP data provisioning tools in

the context of SAP HANA

� Identify the benefits of each data provisioning method

� Gain a working knowledge of some features in a few of the SAP

provisioning tools

Chapter 9 Data Provisioning376

In this chapter, we’ll cover how to provide data to SAP HANA information models

and the basic principles and tools used for data provisioning in SAP HANA. For our

information models to work properly, we need to ensure that we get the right data

into SAP HANA at the right time.

There are people who specialize in each of the data provisioning tools discussed,

and there are special training courses for every one of these tools. Our aim, there-

fore, isn’t to dive deep into detail about the various concepts and tools, but to give

you a high-level working knowledge of what each tool does to help you under-

stand the concepts involved when provisioning data into SAP HANA, to enable

you to have a meaningful conversation on the topic, and to help you choose the

right tool for your business requirements.

Real-World Scenario

You start a new project that involves SAP HANA, and you’re asked to provide

data for the SAP HANA system.

You start by asking important questions about the expected data provision-

ing: Where is the data stored currently? Is the data clean? Will the data be

updated in the source systems, or is this a once-off data provisioning pro-

cess? Does the business need the data in real time, or is a delay of some hours

acceptable? Does the data need to be stored in the SAP HANA system? Does

the business already have a data provisioning tool that you can reuse? Will

the data provisioning process be limited to SAP HANA only?

After evaluating all these factors, you recommend the right data provision-

ing tool to the business, one that is applicable to its budget, circumstances,

and requirements.


The objective of this portion of the SAP HANA certification is to test your high-

level understanding of the various SAP data provisioning tools available for get-

ting data into an SAP HANA system.



topics:

� General data provisioning concepts

� Different SAP data provisioning tools

� Similarities and differences between the various provisioning tools

� When to use a particular SAP provisioning tool

� Features of some of the SAP data provisioning tools

Note



In Chapter 5, we started by looking at the basic information modeling concepts.

We presented an overview diagram of the different types of information models in

SAP HANA and how they all work together to help solve business requirements.

This overview diagram is shown again in Figure 9.1.

So far, we’ve focused our attention on the bottom half of the diagram: on informa-

tion modeling inside SAP HANA. In this chapter, our attention shifts to the upper-

left corner of the diagram. You need to know how to supply your SAP HANA infor-

mation models with the data they require.

SAP HANA doesn’t really differentiate whether the data comes from SAP or from

non-SAP source systems. Data is data, regardless of where it comes from. Combined

with the fact that SAP HANA is a full platform—and not just a dumb database—this

makes it an excellent choice for all companies. SAP HANA is meant not only for tra-

ditional SAP customers but also for a wide range of other companies, from small

startups to large corporations. In fact, SAP works with thousands of startups and

smaller partners, helping them use SAP HANA to deliver innovative solutions that

are “nontraditional” in the SAP sense.

In this chapter, we’ll look at some of the various data provisioning concepts and

tools.


Figure 9.1 Overview Diagram and Where Data Provisioning Fits

Tip

We’ll look at the on-premise data provisioning tools. Several of the data provision tools

are also used for SAP Cloud Platform, such as SAP Cloud Platform Smart Data Integration

and SAP Cloud Platform Integration for data services. For more information on these

tools, look at the SAP Cloud Platform Integration information at http://s-prs.co/v487636.

Concepts

Data provisioning is used to get data from a source system and provide that data to

a target system. In our case, we get this data from various source systems, whether

they’re SAP or non-SAP systems, and our target is always the SAP HANA system.

We’re not too concerned in this chapter about the different types of data—

whether financial transactional data, spatial data, real-time sensor data, social

Data provisioning tool, e.g. SDI

SAP source system

Database

Non-SAP source system

Database

Report or query

HTML5 browser

Star-join view

(“Cube”)

Star-join view

(“Cube”)

Application server

SAP HANA

Calculation view of type cube

Union

Star-join view (“Cube”)Tables

Table 1 Table 8

Table 2 Table 9

Table 3 Table 10

Table 4 Table 11

Table 5 Table 12

Table 6

Table 7 Data Foundation

Apples

Quantity – 10

Laura

Customerdimension view

Productsdimension view

Star Join(Logical Join)



media data, or aggregated information. We’ll focus instead on the process of pro-

viding the data to SAP HANA.

Data provisioning goes beyond merely loading data into the target system. With

SAP HANA and some of its new approaches to information modeling, we also have

more options available to us when working with data from other systems. We

don’t always have to store the data we use in SAP HANA as with traditional data

loading. We now also have the ability to consume and analyze data once, never to

be looked at again.

Extract, Transform, and Load

Extract, transform, load (ETL) is viewed by many people as the traditional process

of retrieving data from a source system and loading it into a target system. Figure

9.2 illustrates the traditional ETL process of data provisioning.

Figure 9.2 Extract, Transform, Load Process

The ETL process consists of the following phases:

� Extraction

During this phase, data is read from a source system. Traditional ETL tools can

read many data sources, and most can also read data from SAP source systems.

� Transform

During this phase, the data is transformed. Normally this means cleaning the

data, but it can refer to any data manipulation. It can combine fields to calculate

something (e.g., sales tax), filter data, or limit data (e.g., to the year 2019). You set

up transformation rules and data flows in the ETL tool.

Load

ETL-based dataprovisioning tool

Transform

Non-SAPsource system

Database

SAP sourcesystem

Database

Extract

Extract

SAP HANA (target)

Tables

Table 1 Table 5

Table 2 Table 6

Table 3 Table 7

Table 4


� Load

In the final phase, the data is written into the target system.

The ETL process is normally a batch process. Due to the time required for the com-

plex transformations that are sometimes necessary, tools don’t deliver the data in

real time.

With SAP HANA, the process sometimes changes from ETL to ELT—extract, load,

transform—because SAP HANA has data provisioning components built in, as

shown in Figure 9.3. In this case, we extract the data, load it into SAP HANA, and

only then transform the data inside the SAP HANA system.

Figure 9.3 Extract, Load, Transform Process

Initial Load and Delta Loads

It’s important to understand that loading data into a target system happens in two

distinct phases, as illustrated in Figure 9.4.

We always start by making a full copy of the data that we need from the source sys-

tem. If we require an entire table, we make a copy of the table in the target system.

This is called the initial load of the data. We can limit the initial load with a filter

(e.g., to load only the latest 2019 data).

Sometimes this step is enough, and we don’t need to do anything more. If we set

up a training or a testing system, we don’t need the latest data in these systems.

Many times, however, we need to ensure that our copy of the data in the target sys-

tem stays up to date. Business carries on, and items are bought and sold. Each new

transaction can change the data in the source system. We need a mechanism to

Data provisioningcomponent inside

SAP HANA

Table

Table

Load

Transform


Database

SAP sourcesystem

Database

Extract

Extract

SAP HANA

Tables

Table 1 Table 5

Table 2 Table 6

Table 3 Table 7

Table 4


update the data in the target system. We could just delete the data in the target sys-

tem and perform the initial load again. For small sets of data, this approach can

work, but it quickly becomes inefficient and wasteful for large data sets.

Figure 9.4 Initial Load of Tables and Subsequent Delta Updates

A more efficient method is to look at the data that has changed and apply the data

changes to our copy of the data in the target system. These data changes are

referred to as the delta, and applying these changes to the target system is called a

delta load. The various data provisioning tools can differ substantially in the way

they implement the delta load mechanism. One method of achieving this is repli-

cation.

Replication

Replication emphasizes the time aspect of the data loading process. ETL focuses on

ensuring that the data is clean and in the correct format in the target system. Rep-

lication does less of that, but it gets the data into the target system as quickly as

possible. As such, the replication process appears quite simple, as shown in Figure

9.5.

SAP HANAtarget system

SAP HANAtarget system

Table

Dataprovisioning

tool

Source system

Table

Table

Source system

Table

Delta data loads

Initial data loads

Dataprovisioning

tool


Figure 9.5 Replication of Tables

The extraction process of ETL tools can be demanding on a source system. Because

they have the potential to dramatically slow down the source system due to the

large volumes of data read operations, the extraction processes are often only run

after hours.

Replication tools aim to get the data into the target system as fast as possible. This

implies that data must be read from the source system at all times of the day and

with minimal impact to the performance of the source system. The exact manner

in which different replication tools achieve this can range from using database

triggers to reading database log files.

With SAP HANA as the target system, we achieve real-time replication speeds.

(We’ve worked on projects where the average time between when a record is

updated in the source system to when the record is updated in SAP HANA was only

about 50 milliseconds!)

Tip

We often use real-time replication with SAP HANA to leverage the speed of SAP HANA for

data analysis.

SAP-Provided Extractors

Normally, we think of data provisioning as reading the data from a single table in

the source system and then writing the same data to a similar table in the target

system. However, it’s possible to perform this process differently, such as reading

Replication Server(real-time)

Read

Read Write

Write


Database

SAP sourcesystem

Database

SAP HANA (target)

Tables

Table 1 Table 5

Table 2 Table 6

Table 3 Table 7

Table 4


the data from a group of tables and delivering all this data as a single integrated

data unit to the target system. This is the idea behind the extractors provided by

SAP.

The standard approach for taking data from an SAP business system to an SAP

Business Warehouse (SAP BW) system (Figure 9.6) uses extractors. SAP predelivers

thousands of extractors for SAP business systems. Extractors are only found in

SAP systems.

In the scenario shown in Figure 9.6, the extractor gathers data from multiple tables

in the SAP source system. It neatly packages this data for consumption by the SAP

BW system in a flat data structure (datasource). Extractors provide both initial load

and delta load functionality. All this happens in the SAP source system.

Figure 9.6 Extractors in SAP Business Systems Loading Data into SAP BW

Now that you understand the concepts, we can visit the various SAP tools available

for provisioning data.

SAP Data Services

SAP Data Services is a powerful ETL tool that can read most known data sources,

transform and clean data, and load data into target systems. SAP Data Services

runs on its own server, as shown in Figure 9.7, and requires a database to store job

definitions and metadata. This can be an SAP HANA database.

SAP BW (target system)

Table 4

View

Table 1 Table 2 Table 3

Table 5

SAP source system

Extractor

Datasource (flat)

Transformation

Activation

Data Store Objector Infoprovider

InfopackageDelta queue


Figure 9.7 SAP Data Services

The extraction capabilities of SAP Data Services are well known in the industry. It

can read many data sources—even obscure ones, such as old COBOL copybooks.

However, SAP Data Services can also read all the latest social media feeds available

on the Internet, and it can communicate with most databases and large business

applications. You can also use it with stores of unstructured data, such as Hadoop.

SAP Data Services can get data from SAP business systems in various ways. We can

use it to directly read a system’s databases. We could also use some of the built-in

extractors we discussed previously.

Another way SAP Data Services can get data from SAP business systems is by using

an ABAP data flow. ABAP is the programming language that SAP uses to write the

SAP Business Suite systems. SAP Data Services can write some ABAP code that you

can load into the SAP source system. This code becomes part of an ABAP data flow

and is a faster and more reliable way to extract the data from an SAP source sys-

tem.

SAP Data Services can also provide complex transformations. SAP Data Services is

actually several products combined. When people refer to SAP Data Services, they

normally refer to the data provisioning product called Data Integrator. Another

product called SAP Data Quality Management is also combined under the SAP Data

Services product name.

All SAP Data Services products ensure that you have clean and reliable informa-

tion to work with. You may have heard the old saying, “garbage in, garbage out.” If

you have rubbish data going into your reporting system, you get rubbish data in

your reports. SAP HANA’s great performance doesn’t make this go away. It just

changes that old saying to, “garbage in, garbage out fast!”

SAP Data Quality Management

ABAP Data Flows

Load

Transform


Database

SAP sourcesystem

Database

Extract

Extract

SAP HANA (target)

Tables

Table 1 Table 5

Table 2 Table 6

Table 3 Table 7

Table 4

Job Server

Designer

SAP Data Services(Data Integrator)


You can use SAP Data Services for data provisioning in the following scenarios:

� A strong ETL tool is required.

� Certain data sources need to be read (e.g., legacy systems).

� Transformation of data is required, for example, when data isn’t clean.

� Real-time data provisioning isn’t a business requirement.

� A customer already has an instance of SAP Data Services running and requires

you to use the current infrastructure and skills available.

� The business requires a tool that delivers data to multiple destinations, of which

SAP HANA is one.

SAP Landscape Transformation Replication Server

SAP Landscape Transformation Replication Server is a data provisioning tool that

uses replication to load data into SAP HANA. Remember that the focus of a replica-

tion server is to load data into the target system as fast as possible.

SAP Landscape Transformation Replication Server started as a tool that SAP devel-

oped and used internally to help companies migrating data between SAP systems,

for example, when companies wanted to consolidate systems after a merger or

acquisition. SAP Landscape Transformation Replication Server is written in ABAP

and runs on the SAP NetWeaver platform, just like SAP ERP and other SAP business

systems. The tool was successfully used for years for data migrations, but it

became more popular and well known with the arrival of SAP HANA.

SAP Landscape Transformation Replication Server works with both SAP and non-

SAP source systems. It uses the same database libraries as the SAP Business Suite

and therefore supports all the same databases as the SAP business systems, includ-

ing SAP HANA, SAP Adaptive Server Enterprise (SAP ASE), SAP MaxDB, Microsoft

SQL Server, IBM DB2, Oracle, and Informix. We recommend that SAP Landscape

Transformation Replication Server be installed on its own server, as shown in

Figure 9.8, even though you can install it inside a source system based on SAP Net-

Weaver.

SAP Landscape Transformation Replication Server uses a trigger-based method for

real-time replication. All databases have a trigger whereby they can spur an action

to occur when data changes in a table. For example, if data is updated or deleted, or

if new data is inserted in a specific table, you can set up a database trigger to also

write these changes to a special replication log table. SAP Landscape Transforma-

tion Replication Server replication is also known as trigger-based replication. The


impact of the SAP Landscape Transformation Replication Server replication on the

source systems is minimal because database triggers are quite efficient.

Figure 9.8 SAP Landscape Transformation Replication Server

SAP Landscape Transformation Replication Server performs the initial load of the

database tables without the use of triggers. The delta load mechanism is trigger

based. The tool creates triggers in the source systems on the tables it replicates.

Any changes made to the source tables after the initial load are collected in logging

tables in a separate database schema. SAP Landscape Transformation Replication

Server then quickly applies all these changes to the SAP HANA target tables and

empties the logging tables in the source system. The logging tables are therefore

normally kept very small.

You can perform filtering and simple transformations as you load the data into

SAP HANA by calling ABAP code inside SAP Landscape Transformation Replication

Server. Because it uses ABAP code, you can create more complex transformations,

but this would interfere with the real-time advantages of SAP Landscape Transfor-

mation Replication Server. If you require complex transformations or data clean-

ing, consider using SAP Data Services.

Because SAP Landscape Transformation Replication Server can read data from

older SAP systems, it can also read non-Unicode data from these systems. It con-

verts this data to Unicode while it’s replicated to SAP HANA. This is required

because SAP HANA uses Unicode.

Source system

Database

Table 1 … Table n

Logging Table 1 Logging Table 2

Trigger

Read module

Controller module

Write module

SAP HANA (target)

Tables

Table 1 Table 5

Table 2 Table 6

Table 3 Table 7

Table 4

SAP LT ReplicationServer


You can use SAP Landscape Transformation Replication Server for your data provi-

sioning needs in the following scenarios:

� Data must be replicated from SAP NetWeaver systems (written in ABAP).

� Replication of data is preferred to transformation.

� There is a business requirement or preference to get the data into SAP HANA in

real time.

� The use of triggers is acceptable.

� Replication of data from older SAP systems is required.

SAP Replication Server

SAP Replication Server also uses replication to load data into SAP HANA. The focus

of a replication server is to load the data into the target system as fast as possible

with minimal impact on the source system.

SAP Replication Server differs from SAP Landscape Transformation Replication

Server in that it uses the database log files for real-time replication, as shown in

Figure 9.9. Its delta load mechanism is log-based. As data changes in tables, all

databases write these changes into a database log file. SAP Replication Server reads

these database log files to enable table replication. SAP Replication Server replica-

tion is also known as log-based replication. This log file replication mechanism has

even less of an impact on the source system than SAP Landscape Transformation

Replication Server.

Figure 9.9 SAP Replication Server

SAP ReplicationServer

Source systemSAP HANA (target)

Table 1 Table 2

Table 3

Database

Table 1 … Table n

Log files

Logs

Tables


SAP Replication Server works with both SAP and non-SAP source systems and sup-

ports many databases. Replication servers don’t focus on the filtering or transfor-

mation of data before loading the data into SAP HANA.

The database log files contain all the changes in a database. SAP Replication Server

can use this information to replicate entire databases. For example, SAP ASE data-

bases use SAP Replication Server to replicate the database log files for disaster

recovery purposes.

Tip

The features of SAP Replication Server, such as log-based replication, have been incorpo-

rated into SAP HANA smart data integration. SAP HANA smart data integration is built in

to SAP HANA and is therefore preferred for our purposes.

You can use SAP Replication Server for your data provisioning needs in the follow-

ing scenarios:

� Replication of data is preferred to transformation.

� There is a business requirement or preference to get the data into SAP HANA in

real-time.

� The use of triggers isn’t acceptable.

� No pool or cluster tables need to be replicated.

� The business requires minimal impact on the source system.

� Database replication is needed for disaster recovery purposes.

� The business requires a tool that delivers data to multiple destinations, of which

SAP HANA is one.

SAP HANA Smart Data Access

SAP HANA smart data access provides SAP HANA with direct access to data in a

remote database. In this case, we don’t store the data in the SAP HANA database;

we can access it at any time. We merely consume the remote data when required.

SAP HANA smart data access allows you to create virtual tables, that is, empty

tables that point to tables or views inside another database somewhere (see Figure

9.10). Some databases have a similar feature called proxy tables (also sometimes

called data federation).


Figure 9.10 SAP HANA Smart Data Access and Virtual Tables

SAP HANA smart data access has various database adapters it uses to connect

these virtual tables to a large number of databases. These adapters include the fol-

lowing:

� SAP HANA, SAP ASE, SAP IQ, SAP Event Stream Processor, and SAP MaxDB

� Hadoop (Hive), Spark, and SAP Vora

� IBM DB2 and IBM Netezza

� Microsoft SQL Server

� Oracle

� Teradata

SAP HANA smart data access can also use an Open Database Connectivity (ODBC)

framework to connect to other databases via ODBC.

When you ask for data from a virtual table, SAP HANA connects to the remote

database across the network, logs in, and requests the relevant data from the

remote table or view. You can combine the data from the remote database table

with data stored in SAP HANA. You can use a virtual table in SAP HANA informa-

tion models, just like native SAP HANA tables. SAP HANA smart data access will

automatically convert the data types from those used in the remote database to

SAP HANA data types.

Report

SAP HANA

SAP HANATable

View

VirtualTable

Remote Database

Table ViewRead and write

via network

SDA


Virtual Tables vs. Native Tables

You can’t expect the same performance from SAP HANA smart data access virtual tables

as from SAP HANA native tables. SAP HANA tables are loaded in the memory of SAP

HANA. The data from the virtual table is fetched from a relatively slow disk-based data-

base across the network.

Keep in mind that SAP HANA smart data access reads data from remote tables

directly, as it’s stored. SAP HANA smart data access can’t do any data cleansing

because it reads data from remote tables. SAP HANA also doesn’t have any sched-

uling tool, so SAP HANA smart data access can’t be used for ETL processes or repli-

cation. It just reads the data from the remote tables as the need arises; for example,

a reporting query requires data to be stored in a remote table.

SAP HANA provides the following optimizations for SAP HANA smart data access

virtual tables:

� SAP HANA caches data that it reads from remote databases. If the data is found

in the cache, the data is read from the cache, and no request is sent to the

remote database.

� SAP HANA can use join relocation. If you join two virtual tables from different

remote databases, it can copy the smaller remote table to a temporary table in

the remote system with the larger table. It then requests the second remote sys-

tem to join the larger table to the temporary table.

� SAP HANA can push down queries to remote databases if it will execute faster in

the remote database, rather than copy lots of data via the network. For example,

a remote database can calculate an aggregate faster on half a million records

than it will take to copy these records across the network for SAP HANA to

aggregate. SAP HANA smart data access collects statistics for all its virtual

sources to help with such optimizations.

� SAP HANA also has optimization features for aggregating data. It uses func-

tional compensation to compensate for certain (missing) features in other data-

bases.

We often hear about people wanting to put all their data into a single physical data

warehouse to analyze it there. Some variations are called data lakes. This scenario

is referred to as big data (see Figure 9.11, left).


Figure 9.11 Logical Data Warehouse Use Case for SAP HANA Smart Data Access

There are a few problems with the physical data warehouse approach:

� You have to duplicate all your data, so your data is double the size, and you now

need more storage. You essentially have doubled your cost and effort.

� You have to copy all that data via the network, which slows your network.

� The data in the new data warehouse is never up to date, and, as such, the data is

never consistent.

� The single data warehouse system itself can’t handle all the different types of

data requirements, such as unstructured data, graph engine data, key–value

pairs, spatial data, huge varieties of data, and so on.

These are some of the issues we deal with in the physical data warehouse scenario;

therefore, it might be better to consider a logical data warehouse. You keep the

data in your source systems, don’t duplicate it via the network, and don’t store

System 3 Database 2 System 3 Database 2

Report 1 Report 2 Report 1 Report 2

System 2System 1 Database 1 System 2System 1 Database 1

Physical Data Warehouse

Table 4

Table 1 Table 2 Table 3

Table 5

Logical Data Warehouse(SAP HANA using SDA)

VirtualTable 4

VirtualTable 1

VirtualTable 2

VirtualTable 3

VirtualTable 5


copies of it. This is where SAP HANA smart data access fits in. You create virtual

tables for the various data sources. None of the data is stored inside SAP HANA, but

yet it behaves just as if it was in a single data warehouse. You get the data from the

various source databases only when required. You can access bits of specific data,

combine them with other data, and report on the data (see Figure 9.11, right).

You can use SAP HANA smart data access for your data provisioning needs in the

following scenarios.

� All the data doesn’t need to be stored inside SAP HANA.

� A logical data warehouse is used, or you’re presented with a big data scenario.

� Integration with Hadoop is required.

� A smaller SAP HANA system is in use that can’t store all the data.

� Real-time data provisioning isn’t a business requirement.

� SAP HANA performance isn’t required.

� You have no need to transform or clean data.

SAP HANA Smart Data Integration and SAP HANA Smart Data Quality

SAP HANA smart data integration and SAP HANA smart data quality are optional

SAP HANA components that you can purchase. They are, however, included with

the SAP HANA, express edition, which is completely free for both development

and production use for up to 32 GB of memory.

Figure 9.12 SAP HANA Smart Data Integration and SAP HANA Smart Data Quality

As shown in Figure 9.12, SAP HANA smart data integration and SAP HANA smart

data quality can work similarly to ELT (refer to Figure 9.3). However, SAP HANA

smart data integration can also be used for real-time replication and a few other

SDI andSDQ


Database

SAP sourcesystem

Database

Table

Table

Extract, replicate,or SDA

Transform(.hdbflowgraph)

Load

SAP HANA

Tables

Table 1 Table 5

Table 2 Table 6

Table 3 Table 7

Table 4


scenarios. Over time, SAP has incorporated many of the data provisioning features

from other tools into SAP HANA smart data integration.

The other fact that counts in favor of SAP HANA smart data integration is that

many of SAP’s business systems have migrated to run on SAP HANA. SAP HANA

smart data integration has evolved so much over the past few years, that you can

apply the simple rule, “When in doubt, use SAP HANA smart data integration.” It’s

even the preferred integration tool on SAP Cloud Platform.

Using various adapters, including SAP HANA smart data access adapters, we can

replicate or consume data in SAP HANA. Then, using SAP HANA smart data quality

capabilities, we can transform and clean that data.

Note

You can find more information about SAP HANA smart data integration at http://s-

prs.co/v487637.

SAP HANA Smart Data Integration

SAP HANA smart data integration can use various adapters to replicate, load, or

consume data in SAP HANA. Via SAP HANA smart data access adapters, SAP HANA

smart data integration can consume data from a large list of databases using vir-

tual tables. SAP HANA smart data integration has some real-time adapters, for

example, reading the database log files of source systems such as Oracle, Microsoft

SQL Server, and IBM DB2 databases. These same adapters can be used to read data

from SAP systems such as SAP ERP. SAP HANA smart data integration also has a

real-time adapter for Twitter, as well as batch adapters for reading web services via

OData and for reading data from Hadoop Hive. There is even an adapter for Micro-

soft Excel.

The difference between the log-based adapters for SAP HANA smart data integra-

tion and SAP Replication Server is that SAP HANA smart data integration has SAP

HANA as the target system for your data provisioning, whereas a standalone SAP

Replication Server system can have many target systems.

Tip

SAP HANA smart data integration has become the preferred SAP data provisioning tool

for SAP. This is due to its wide range of adapters that provide both batch and real-time

replication capabilities, and the fact that many SAP systems are now running on SAP

HANA.




SAP HANA Smart Data Quality

SAP HANA smart data quality works together with SAP HANA smart data integra-

tion and focuses on the data cleaning aspects. It also provides complex transfor-

mations of your data. The data flows in SAP HANA smart data quality use the

.hdbflowgraph features inside SAP HANA, as shown in Figure 9.13. (We’ll also use

this for predictive and geospatial models in Chapters 10 and 11, respectively.)

These data flows work differently from data flows in SAP Data Services. Therefore,

you can’t simply copy and paste your SAP Data Services data flows into SAP HANA

smart data quality.

On the left side of the flowgraph in Figure 9.13, you create your Data Sources. This

is where you set up your SAP HANA smart data integration sources. You then

design a flow for your data, until it ends up in the Data Target on the right side. Part

of this flow can use SAP HANA smart data quality to clean and transform the data.

Figure 9.13 Flowgraph Using SAP HANA Smart Data Integration and SAP HANA Smart Data Quality

The Geocode node can be used to transform addresses to map locations or trans-

form map locations to addresses. This forms part of the data transformation in the

ELT process. In fact, you can use all the different nodes, shown in Figure 9.14 in a

flowgraph for data transformation, including all the predictive algorithms avail-

able in SAP HANA.

In the top right of Figure 9.14, you see nodes for Data Quality. There are also some

in the Advanced section. In the Basic and Flow Control sections, the nodes provide


similar functionality as some of the nodes in a calculation view, such as Projection,

Join, Union, and Aggregation.

Figure 9.14 Available Flowgraph Nodes

You can use SAP smart data integration and SAP HANA smart data quality for your

data provisioning needs in the following scenarios:

� An ELT tool is required.

� Transformation of the data is required, such as when the data isn’t clean.

� Real-time data provisioning might be a business requirement.

� Replication of data is required, with later transformation of the data.

� The data provision target is SAP HANA.

� Data must be read from many data sources.

SAP HANA Streaming Analytics

SAP HANA streaming analytics provides SAP HANA with the ability to observe, pro-

cess, and analyze fast-moving data. It extends the capabilities of the SAP HANA

platform with the addition of real-time event stream processing.

An example of such fast-moving data is sensor data or financial ticker tape data.

It’s a continuous stream of data points, for example, the temperature of a

machine. Each data point in the stream is linked to a point in time. This combina-

tion of the temperature data at a specific point in time is called an “event.”


Just like SAP HANA smart data integration and SAP HANA smart data quality, SAP

HANA streaming analytics is an optional SAP HANA component that you can pur-

chase. However, SAP HANA streaming analytics is included with SAP HANA,

express edition, which is completely free for both development and production

use for up to 32 GB of memory.

Let’s look at an example of why you might use data stream processing. Say that

you want to monitor some manufacturing equipment. Sensor data tells you the

equipment temperature, but that data alone isn’t very interesting. If the equip-

ment temperature is 90°C, and the pressure is high, that might be normal. How-

ever, if the temperature is 180°C when the pressure is high, that could indicate a

dangerous situation in which equipment could be blown apart, causing accidents

and manufacturing losses. You want to receive an alert before a situation becomes

critical so you can analyze the temperature and pressure trends over time and the

relationship between these factors. It’s the combination of certain events in a

specified time frame that is important here. We call this the “event window.”

The temperature data itself isn’t important, and you don’t want to store this data.

The data, once exposed and processed, is no longer useful. You just want to find

the trend, see the relationships between the data, and react when certain condi-

tions are met within a certain time period.

Data streaming and the processing of such information is different from a single

event, such as a database trigger. Figure 9.15 illustrates the data streaming process.

Figure 9.15 SAP HANA Streaming Analytics

Another example to illustrate the use of SAP HANA streaming analytics is in stock

trading. You can have many streams of financial data coming in at very high

speeds. How do you react to these continual data streams in a short span of time?

Productionplant system

Financial tickertape system

History data

SAP HANA

Streaming data

SAP HANA streaming analytics

Event window


What do you react to? A single stock going down may not be a problem, but if

many stock prices start going down quickly within a certain time window—say,

five minutes—that could indicate a stock market crash. The single price values are

of little importance. The important information is found in combining many

trends and finding the trend in a certain time window.

The Internet of Things (IoT) isn’t in the future—it’s already here. Millions of devices

are now connected and delivering information. We even have lightbulbs with

built-in Wi-Fi that can be controlled from an app on your smartphone. How do you

take advantage of all the data that’s available? How do you collect it and analyze it?

With SAP HANA streaming analytics, not only can you analyze data streams but

you also can store this data and perform deeper, wider analytics of it at a later

stage. In this way, SAP HANA streaming analytics and SAP HANA complement

each other. SAP HANA streaming analytics is also a vital part of the SAP Leonardo

technology stack, where SAP looks at innovative solutions touching areas such as

the IoT, machine learning, and blockchain.

You can use SAP HANA streaming analytics for your data provisioning needs in

the following scenarios:

� Real-time streaming data for events in a certain time window must be analyzed

and reacted to properly.

� Fast-moving data is observed, processed, and analyzed, but not stored.

� Data is processed as-is; there is no data cleaning, but you have to find a signal in

spite of the noisy data.

� Only data where something interesting is happening needs to be stored. The

rest of the data gets discarded, resulting in huge storage savings.

SQL Anywhere and Remote Data Sync

Up to this point, we’ve made the assumption that we always have good, stable, and

constant connectivity between the data sources and SAP HANA. Unfortunately,

that isn’t always the case.

Let’s consider some scenarios where we can’t assume that we have such connectiv-

ity:

� Remote workplaces, such as ships, trains, oil rigs, and mines, that lose connec-

tivity for periods of time (also known as satellite servers)


� Mobile applications that get used in factories, underground, or any place where

the mobile device loses connectivity due to interference or layers of concrete

and metals

� IoT sensors that have to conserve power to keep running as long as possible, as

well as where constant connectivity would be huge drain on energy levels

For these cases, you can install SAP SQL Anywhere on these remote servers or

devices. This product contains a small database that can even be embedded in

hardware devices such as sensors.

On the SAP HANA side, we install SAP HANA remote data sync. You then configure

the SAP HANA remote data sync server in SAP HANA to communicate with the

SAP SQL Anywhere at the remote sites, as shown in Figure 9.16.

SAP HANA remote data sync is particularly useful when you use two-way data

flows. The remote sites can send some data to the SAP HANA server, where it can

get processed. The results can then be sent down to the remote site and are stored

as master data. When the connectivity disappears, the remote site can happily con-

tinue functioning until connectivity is restored. All transactions in that time

frame are then synchronized.

Figure 9.16 Using SAP HANA Remote Data Sync and SAP SQL Anywhere for Remote Sites with Occa-sional Connectivity

We can use this setup to synchronize data between SAP HANA and thousands of

remote SAP SQL Anywhere databases, and still ensure that we can acquire data for

analysis in SAP HANA over intermittent and slow networks. The remote sites ben-

efit by always having data available for their business applications.

Data CenterRemote sites

Satellite Server

MobileInternet of Things SAP HANA platform

RDSync Server

SAP HANAdatabase

SAP SQLAnywhere

Applications

Reports

DataSynchronization


Flat Files

Flat files (also called comma-separated values [CSV] files) are the simplest way to

provision data to SAP HANA. Especially in proof-of-concept (POC) or sandbox test-

ing projects, you can waste time connecting SAP HANA to the various source sys-

tems. You don’t need the latest data; you just want to find out if an idea you have

for information modeling will work.

As such, it makes sense to ask the business to provide you with the data in flat files,

and then you can load the data into SAP HANA for the POC or sandbox develop-

ment (see Figure 9.17).

Figure 9.17 Loading Data into SAP HANA from Text Files Using Core Data Services (CDS)

In SAP Web IDE for SAP HANA, you create an .hdbtabledata file. You can see an

example in Figure 9.18 from the SAP HANA Interactive Education (SHINE) demo. In

line 7, it specifies that the file is a CSV file, and line 8 specifies the name of the text

file.

The entire SHINE demo application is built this way, and it loads the demo data

using this method.

SPS 04

One of the features that you could do with SAP HANA Studio in SAP HANA 1.0 was to

import data into a table from CSV files or Excel spreadsheets. With SPS 04, you can now

import data from medium-sized CSV files or Excel spreadsheets using the SAP HANA

database explorer.

CSV data file

SAP HANA

Tables

Table 1 Table 5

Table 2 Table 6

Table 3 Table 7

Table 4

Import data files


Figure 9.18 Loading Flat Files into SAP HANA Using an .hdbtabledata Definition

Consider using data files for your data provisioning needs in the following scenar-

ios:

� Data only needs to be loaded once, for example, for sandbox or training sys-

tems.

� There is no connection available to the source systems.

� Data needs to be loaded simply and quickly.

Web Services (OData and REST)

We won’t discuss web services in detail in this text, but of course you can consume

data from a source system that provides it via web services. You can consume

these web services using the SAP HANA extended application services, advanced

model (SAP HANA XSA) development environment.


SAP HANA XSA not only delivers OData and REST web services but also consumes

them, as shown in Figure 9.19. The data is normally transferred in JavaScript Object

Notation (JSON) format rather than XML format.

Figure 9.19 Data Provisioning via Web Services

Consider using web services for your data provisioning needs when the source sys-

tems only expose their data as web services. This is typical of web and cloud appli-

cations.

SAP Vora

Another assumption that we sometimes unconsciously make is that all the data

we work with is structured and neatly arranged in database records. Real life tends

to be a bit messier.

Hadoop has grown quite popular in past years for addressing the problems of stor-

ing and working with large volumes of “messy” data. Think of Google having to

work with all the “messy” data on the Internet.

Businesses have started using Hadoop for its low-cost distributed storage and abil-

ity to scale. Hadoop is basically a distributed data infrastructure that distributes

large data collections across multiple nodes within a cluster of low-cost servers.

However, Hadoop isn’t very good for online analytical processing (OLAP), which

businesses use to analyze and derive meaning from their data. It usually entails

long-running batch computations, and real-time decision support capabilities just

aren’t possible. Business analysts sometimes also need to wait for the Hadoop data

to be transformed by a few highly skilled but scare resources before they can use it.

XSengine

Database

SAP HANA

Tables

Table 1 Table 5

Table 2 Table 6

Table 3 Table 7

Table 4

Source system

Web Services ConsumeWeb Services(OData, REST)


SAP Vora is the in-memory solution that SAP has built to solve these problems.

SAP Vora uses the in-memory technologies that SAP developed with SAP HANA to

speed up these long-running analytics in Hadoop.

SAP Vora is installed on each Hadoop/Spark node, as shown in Figure 9.20. Spark is

a component running on top of Hadoop to speed up the processing of data. SAP

Vora takes that to the next level.

Figure 9.20 SAP Vora as Part of a Hadoop/Spark Cluster

Inside SAP Vora is a set of in-memory distributed computing engines for running

high-performance, sophisticated analytics against relational, time series, graph,

and JSON data. It has an OLAP engine for creating the analytics that businesses

require.

When the data in SAP Vora exceeds the memory, it can swap the data to disk when

required. These data overflows are processed efficiently, while ensuring that the

resulting analysis still reflect the complete data set.

SAP HANA Data Management Suite

In this chapter you might get the impression that you only need to get the data

into the SAP HANA system, but you’ll also use many of these tools for maintaining

Hadoop/Spark cluster

Hadoop/ Spark node

SAP Vora

Files Kubernetescluster

Spark

Hadoop/ Spark node

Files

Spark

Hadoop/ Spark node

Files

Spark

Hadoop/ Spark node

Files

Spark

Users

Data ScienceVisual AppsBusiness

Intelligence Predictive

SAP Vora SAP Vora SAP Vora


the data. This maintenance might be to keep data up to date, especially for master

data in the case of master data management. Or it might be due to regulations

such as General Data Protection Regulation (GDPR) that force you to check,

update, and delete user data.

Data is often called the “oil” of this century. Even engines need some fresh oil

every now and then. SAP released SAP HANA Data Management Suite in 2018 to

help with this kind of data maintenance and to help with managing data from

multiple sources, such as legacy databases, cloud systems, desktop programs, sen-

sors, and data lakes. You can then enrich that data with spatial, text, graph, predic-

tive, and series processing, which we discuss in the next two chapters.

Many of the tools in SAP HANA Data Management Suite are available individually.

In the suite they are used together to manage the data for the entire enterprise.

Tools included are as follows:

� SAP HANA for in-memory processing of the data, ETL, modeling, development,

data anonymization, security, and much more.

� SAP Enterprise Architecture Designer (SAP EA Designer) for defining an enter-

prise-wide metadata model to manage the data models and data rules, and cre-

ate a harmonized view of the data. (We discussed SAP EA Designer in Chapter 4.)

� SAP Cloud Platform Big Data Services to manage the big data services in the

cloud. This includes SAP Vora that we discussed in this chapter.

� SAP Data Hub for providing a central system for data governance, orchestra-

tion, pipelining, integration, and federation of data in the company.


In this chapter, we covered the following important terms:

� Data provisioning

Get data from a source system and provide that data to a target system.

� Extract, transform, load (ETL)

The ETL process consists of the following phases:

– Extraction: Data is read from a source system. Traditional ETL tools can read

many data sources, and most can also read data from SAP source systems.

– Transform: Data is transformed. Normally this means cleaning the data, but

it can refer to any data manipulation.

– Load: Data is written into the target system.


� Initial load

The process of making a full copy of data from the source system. If we require

an entire table, we make a copy of the table in the target system.

� Delta load

Look at the data that has changed, and apply the data changes to the copy of the

data in the target system. These data changes are referred to as the delta, and

applying these changes to the target system is called a delta load.

� SAP extractors

Read data from a group of tables and deliver the data as a single integrated data

unit to the target system.

� SAP Data Services

Solution used to integrate, improve the quality of, profile, process, and deliver

data.

� SAP Landscape Transformation Replication Server

Uses replication to load data into SAP HANA. Remember that the focus of a rep-

lication server is to load data into the target system as fast as possible.

� SAP Replication Server

Uses replication to load data into SAP HANA. SAP Replication Server differs

from SAP Landscape Transformation Replication Server in that it uses the data-

base log files for real-time replication. SAP Replication Server reads these data-

base log files to enable table replication. SAP Replication Server replication is

also known as log-based replication.

� SAP HANA smart data access

Provides SAP HANA with direct access to data in a remote database.

� SAP HANA smart data integration

Uses various adapters to replicate, load, or consume data in SAP HANA.

� SAP HANA smart data quality

Used to cleanse and enrich data persisted in the SAP HANA database.

� SAP HANA streaming analytics

Allows you to observe, process, and analyze fast-moving data. You can create

continuous projects in Computation Continuous Language (CCL) to model

streaming information. You can then issue continuous queries to analyze the

data.

� SAP HANA remote data sync

Allows you to synchronize data between SAP HANA and thousands of remote

SAP SQL Anywhere databases, while still ensuring that you can acquire data for

analysis in SAP HANA over intermittent and slow networks.


� SAP Vora

Uses the in-memory technologies that SAP developed with SAP HANA to speed

up long-running analytics in Hadoop and Spark clusters.

Practice Questions

These practice questions will help you evaluate your understanding of the topic.

The questions shown are similar in nature to those found on the certification

examination. Although none of these questions will be found on the exam itself,

they will allow you to review your knowledge of the subject. Select the correct

answers, and then check the completeness of your answers in the “Practice Ques-

tion Answers and Explanations” section. Remember, on the exam, you must select

all correct answers and only correct answers to receive credit for the question.

1. Which of the following SAP data provisioning tools can provide real-time data

provisioning? (There are 3 correct answers.)

� A. SAP HANA smart data integration

� B. SAP Data Services

� C. SAP Replication Server

� D. SAP HANA smart data quality

� E. SAP Landscape Transformation Replication Server

2. What is the correct sequence for loading data with SAP Data Services?

� A. Extract • Load • Transform

� B. Load • Transform • Extract

� C. Load • Extract • Transform

� D. Extract • Transform • Load

3. Which of the following data provisioning tools can provide a delta load of a

table from the source system?

� A. SAP HANA streaming analytics

� B. SAP HANA smart data access

� C. SAP Landscape Transformation Replication Server

� D. Flat files


4. Which of the following data operations can you perform with SAP HANA

smart data access? (There are 2 correct answers.)

� A. Data federation

� B. Logical data warehouse

� C. Physical data warehouse

� D. Real-time replication

5. You want to retrieve data from a web application. Which of the following

could you use? (There are 3 correct answers.)

� A. JSON

� B. REST

� C. JDBC

� D. BICS

� E. OData

6. Which data provisioning tools can use SAP HANA adapters? (There are 2 cor-

rect answers.)

� A. SAP HANA smart data access

� B. SAP HANA streaming analytics

� C. SAP HANA smart data integration

� D. SAP Replication Server

7. Which of the following data provisioning tools can you use to read data

directly from Twitter? (There are 3 correct answers.)

� A. SAP Replication Server

� B. SAP HANA XS engine

� C. SAP Data Services

� D. SAP HANA smart data integration

� E. SAP HANA smart data access


8. Which SAP tools can work with data from Hadoop? (There are 3 correct

answers.)


� B. SAP Landscape Transformation Replication Server


� D. SAP HANA remote data sync

� E. SAP Vora

9. The company asks you to get data from IoT sensors. Which SAP tools could

you use? (There are 3 correct answers.)

� A. SAP SQL Anywhere

� B. SAP HANA remote data sync

� C. SAP Landscape Transformation Replication Server

� D. SAP HANA streaming analytics

� E. SAP HANA smart data access

10. Which SAP tool can you use to include geospatial and predictive processing as

part of the data transformation process?


� B. SAP HANA smart data quality


� D. SAP Data Services

11. What type of file would you create in SAP Web IDE for SAP HANA to do data

cleaning?

� A. .hdbtabledata

� B. .hdbgraphworkspace

� C. .hdbcds

� D. .hdbflowgraph



1. Correct answers: A, C, E

SAP HANA smart data integration, SAP Replication Server, and SAP Landscape

Transformation Replication Server. See Table 9.1 at the end of this chapter for a

quick reference.

SAP HANA smart data quality works together with SAP HANA smart data inte-

gration but focuses on cleaning the data, not replicating the data.


Extract • Transform • Load. SAP Data Services is an ETL tool.


SAP Landscape Transformation Replication Server provides delta loads.

SAP HANA streaming analytics performs neither initial nor delta loads because

it streams data continually.

SAP HANA smart data access and flat files can only be used to perform an initial

load.


SAP HANA smart data access can be used for data federation and as a logical

data warehouse.

SAP HANA smart data access isn’t used to load data for a physical data ware-

house because there is no delta load mechanism.

SAP HANA smart data access can’t perform real-time replication because there

is no delta load mechanism.

5. Correct answers: A, B, E

JSON, REST, and OData are all associated with web services.

Web applications (in the cloud or on the Internet) don’t normally allow Java

Database Connectivity (JDBC) database connections.

Web applications aren’t necessarily SAP systems that understand BICS. Again, a

web application won’t allow database connections via the Internet for security

reasons.


SAP HANA smart data access and SAP HANA smart data integration can use the

SAP HANA adapters. To use the built-in adapters, the data provisioning tools

also have to be built in.

SAP HANA streaming analytics reads streaming data, but not by using the

adapters.


SAP Replication Server reads database log files.

7. Correct answers: B, C, D

Twitter does have web services. This isn’t specified in the book, so in the actual

exam, this option would not appear because it makes too many assumptions.

For the purposes of this book, we assumed that everyone knows what Twitter is

and that it has web services.

SAP Data Services can read social media feeds, as discussed in the Extracting

Data section.

SAP HANA smart data integration has a Twitter adapter. See the SAP HANA

Smart Data Integration section for more information.

8. Correct answers: A, C, E

SAP HANA smart data access, SAP HANA smart data integration, and SAP Vora

can work with data from Hadoop.

SAP Landscape Transformation Replication Server can only use SAP ABAP sys-

tems as the data source.

SAP HANA remote data sync connects to remote SAP SQL Anywhere databases.


You can use SAP SQL Anywhere to store the IoT sensor data, and you then use

SAP HANA remote data sync to get it to SAP HANA.

SAP HANA streaming analytics is often used to stream sensor data to SAP HANA.

SAP Landscape Transformation Replication Server uses SAP systems as data

sources.

SAP HANA smart data access doesn’t connect to sensors (unless some fancy

new device embeds a database!).


You can you use SAP HANA smart data quality to include geospatial and predic-

tive processing as part of the data transformation process.

SAP HANA smart data access doesn’t have transformation capabilities.

SAP HANA smart data integration is used to get the data to SAP HANA smart

data quality.

SAP Data Services doesn’t have predictive processing as part of the data trans-

formation process.


You create an .hdbflowgraph file in SAP Web IDE for SAP HANA to do data clean-

ing.


.hdbtabledata is used to load CSV files into SAP HANA.

.hdbgraphworkspace is used for graph processing.

.hdbcds is used, for example, to create tables using CDS.

Takeaway

You should now have a high-level understanding of the various SAP data provi-

sioning tools available for getting data into an SAP HANA system, and you should

know what each one does.

When asked to recommend an SAP data provisioning tool during a project, you

can use Table 9.1 as a quick reference.

Summary

You’ve learned the various data provisioning concepts and looked at the different

SAP data provisioning tools available in the context of SAP HANA. They all have

their own strengths, and you now know when to use the various tools.

In the next two chapters, we’ll start using exciting features in SAP HANA for

searching and analyzing text, leveraging predictive library functions using spatial

features, creating graph models for analyzing relationships, and processing IoT

data using series data.

Business Requirement Data Provisioning Tool

Real-time replication SAP Landscape Transformation Replication Server, SAP

Replication Server, or SAP HANA smart data integration

ETL, data cleaning SAP Data Services, or SAP HANA smart data quality

Extractors SAP Data Services

Streaming data SAP HANA streaming analytics

Federation, big data SAP HANA smart data access

POC, development, testing Flat files

Remote sites, or occasionally

connected devices

SAP HANA remote data sync

Table 9.1 Choosing a Data Provisioning Tool

Chapter 10

Text Processing and Predictive Modeling


� Know how to create a text search index

� Discover fuzzy text search, text analysis, and text mining

� Get to know the types of prediction algorithms

� Learn how to create a predictive analysis model

Chapter 10 Text Processing and Predictive Modeling412

Now that you understand the basics of SAP HANA information modeling, it’s time

to take your knowledge to the next level. In this chapter, we’ll cover two different

topics: text processing and predictive modeling. In the next chapter we’ll discuss

spatial, graph, and series data modeling. These five topics are independent from

each other but can be combined in projects; for an example, see the Real-World

Scenario box.

In this chapter and the next, we’ll do a deep dive into some advanced modeling

with SAP HANA. Each topic that we’ll discuss is a specialist area on its own, on

which people build their careers. We obviously won’t go that deep into these top-

ics, but you’ll have a good base for more detailed learning on any of these topics at

the end of these two chapters.

These two chapters probably include the most changes from the previous edition.

In the second edition, these topics were in one chapter, as they were only 10% of

the certification exam. SAP Education has expanded its material on advanced

modeling with SAP HANA to a separate three-day training course named HA301,

and it’s now 25% of the certification exam.

Most of these advanced topic areas are components of SAP HANA that are only

available in SAP HANA, enterprise edition. The enterprise edition contains every-

thing from the standard edition and components such as text processing, predic-

tive modeling, and spatial, graph, and streaming analytics.

SAP HANA, express edition, has all of these components and is a free edition for

both development and production with usage up to 32 GB. You can easily expand

up to 128 GB by purchasing more memory usage. To move beyond 128 GB, you’ll

need to upgrade to a full-use license edition.

We’ll begin our discussion with text modeling—specifically, how to search and

find text even if misspelled, how to analyze text to find sentiment, and how to

mine text in documents. Then, we’ll look at how to build predictive analysis mod-

els for business users using a powerful built-in library and graphical web-based

tools.

Real-World Scenario

You’re working on an SAP HANA project at a pharmaceutical company.

The company has created a mobile web application with SAP HANA for its

sales representatives. However, users are struggling to find products on

their mobile phones because the keyboard is small, and the names of the


pharmaceutical products are quite difficult to type correctly. Therefore,

you implement a fuzzy text search that allows users to enter roughly what

they want, and the SAP HANA system finds the correct products. The com-

pany later tells you that this also helped users avoid adding duplicate

entries into the system.

You improve this mobile application by suggesting products to the sales

reps. You create a predictive analysis model in SAP HANA to help with sales

forecasting by looking at the seasonal patterns to determine when people

use certain products more (e.g. , in the winter).

You also notice that there were many comments about the company’s prod-

ucts on Facebook and Twitter. You process these comments in SAP HANA

and can quickly tell the marketing department which products people are

talking about, as well as alert the company if people are making negative

comments about any product. You provide special alerts to the sales reps to

inform the customers about these products.

And, of course, you win the “Best Employee” award for the year!



knowledge of advanced modeling with SAP HANA.


topics:

� How to create a text search index

� Fuzzy text search and text search queries

� Text analysis and the text analytics configurations

� Text mining

� The types of prediction algorithms

� How to create a predictive analysis model

Note




In recent years, the field of text and language processing has expanded dramati-

cally. Topics such as talking computers that were previously only found in science

fiction stories now fill the media. The daily technology news articles talk glowingly

of chatbots, artificial intelligence (AI), natural language processing (NLP), text-to-

speech, and speech-to-text. Many people have voice-based “assistants” in their

homes and on their phones where they regularly talk to Alexa, Siri, and the Google

Assistant. Automatic language translation is improving due to new deep learning

systems and powerful custom-made processors.

You often have to help set the right expectations when working in a project. With

all the media attention, it’s easy to get caught up in believing you can do every-

thing with such language technologies. While some of it works well for shorter

sentences, this won’t replace translators or editors. Even if it’s 99% correct, this

paragraph would have at least one major mistake in it, let alone the subtler

nuances of language, such as intonation, double meanings, irregular verbs, puns,

oxymorons, irony, satire, and sarcasm. For example, “I named my dog five miles

so I can tell people that I walk five miles every day.” Or, “Help stamp out, eliminate,

and abolish redundancy!”

In the next section, we’ll focus on some of the text processing features built into

SAP HANA. SAP also has chatbots, robotic process automation (RPA), and machine

learning, but we won’t discuss these here.

Text Processing

As you know, in Google, you type in a few words, and the Google search engine

returns a list of web pages that best meet your search query, ranked to your prefer-

ences. It used to indicate when you typed a word incorrectly and suggested

another spelling with the question, Did you mean…?. Now, however, the Google

search engine has become so confident that it no longer suggests the correction

with a qualifying question, but instead says Showing results for…. If you’re con-

vinced your original entry is correct, it will allow you to Search instead for [what-

ever you entered]. Even more revolutionary is the search engine’s ability to predict

what you want as you type.

In the following, we’ll first provide you with a brief overview of text processing.

We’ll then look at working with the text features in SAP HANA—namely, text

searches, text analysis, and text mining. However, before we can discuss each of


these topics in more detail, we first need to create a full-text index. As you’ll see in

Figure 10.1, the full-text index is the backbone of all these processes.

Text Processing Overview

Google search is a good example of what you can do with SAP HANA in your infor-

mation models. We can perform fuzzy text searches, such as the “Did you mean”

example, and we can perform text mining to find the best document out of a large

collection for a user. We can also use text analysis to find out if people wrote some

text with a positive or negative sentiment.

Figure 10.1 provides an overview of the various text processing processes in SAP

HANA. We can use SAP HANA text processing on any table column that has some

text data included. In this case, we want to process the data in the Description field.

This column could have data in a NVARCHAR field, or documents such as PDFs stored

in a BLOB or TEXT field. We have to prepare the data in this field before we can run

text processing queries on it. There are three main scenarios for querying the text.

We’ll discuss each of these in more detail in upcoming subsections:

� Text search

This feature is used when we want to find out which records contain some

search terms. To get this working, we first need to create a full-text index for the

Description field. The full-text index is created in a hidden column in the table

and can only be accessed indirectly via the SAP HANA text processing features.

When SAP HANA creates the full-text index, it first processes the files contained

in the Description field if we store documents in it. It can extract the text from

these documents, can detect the language the text is written in, and break up

the text in words or terms—a process called tokenization. Lastly, SAP HANA can

convert these tokens into their root words using a process called stemming.

� Text analysis

This feature extends the functionality of the full-text index. When creating the

full-text index, we need to specify a text analysis configuration file and activate

text analysis, which is used when we want to find out the sentiment of the text

or to extract more semantic details from the text. With text analysis, SAP HANA

can find parts-of-speech, group nouns, and extract entities and facts. (Noun

groups are words that are often used together, for example, global economy or

fish and chips.) All these results are stored in a new table with a $TA_ prefix.

� Text mining

This feature extends the functionality of the full-text index and text analysis

results. Where text searching and text analysis work within a document, text


mining works across and with multiple documents. When we create the full-

text index, we need to specify a text mining configuration file and activate text

analysis and text mining. Text mining is used when we want to work with, clas-

sify, and cluster stored documents in our table columns. We can run queries to

find documents that contain certain search terms, and SAP HANA can also find

any similar or related documents for us. These results are stored in new tables

with a $TM_ prefix. The only table you should use is prefixed with $TM_MATRIX_.

This table is also known as the text mining index and contains a term document

matrix. Text mining is optimized through the results of the full-text index and

the text analysis, and it uses tokenization, stemming, part-of-speech tagging,

and entity extraction.

Figure 10.1 Overview of Text Processing in SAP HANA

Text Index

To enable the text features in SAP HANA, you need a full-text index. Depending on

what we specify when we create the full-text index, it gives access to one or more

text features. The full-text index will use additional memory to provide the

requested text features.

ID Title Field 1 Field 2 …

Table with text or documents

Full text index

Text Analysis Configuration Text Mining Configuration

$TA_ table with text analysis results $TM_ tables with text mining results

Description(text or

document)

$TM_MATRIX_ table for text document matrix(also known as “text mining index”)

Optimized through results of text analysis (tokens, stems, part-of-speech tags, entities)

• Part-of-speech tagging• Noun groups• Entity extraction• Fact extraction

• File proc.

• Languagedetection

• Tokens

• Stems

(hiddencolumn)


Listing 10.1 provides an example of a SQL statement to create a full-text index

named DEMO_TEXT_INDEX on a column called SOME_TEXT in a column table. This full-

text index enables the SOME_TEXT column for text search and analysis.

CREATE FULLTEXT INDEX DEMO_TEXT_INDEX ON"DEMO_TEXT" ("SOME_TEXT")LANGUAGE DETECTION ('EN')SEARCH ONLY ONFUZZY SEARCH INDEX ONTEXT ANALYSIS ON

CONFIGURATION 'EXTRACTION_CORE_VOICEOFCUSTOMER'TEXT MINING OFF;

Listing 10.1 SQL Statement to Create a Full-Text Index

Let’s examine the different text elements of the SQL statement in Listing 10.1:

� CREATE FULLTEXT INDEX

The first line creates a full-text index from the SOME_TEXT column in the DEMO_

TEXT table.

� LANGUAGE DETECTION

SAP HANA will attempt to detect the language of your text. You can specify on

what languages it should focus with this clause. In this case, we indicated that

our text is all in English.

� SEARCH ONLY ON

This clause saves some memory by not copying the source text into the full-text

index.

� FUZZY SEARCH INDEX ON

This enables or disables the fault-tolerant search feature. This feature is also

called fuzzy text search. You can speed up the fuzzy search on string types by

creating an additional data structure called a fuzzy search index. However, you

should be aware that this additional index increases the memory requirements

of the table. In addition, don’t confuse the fuzzy search index with the full-text

index; it’s just an additional data structure to the full-text index.

� TEXT ANALYSIS ON

This enables or disables the text analysis feature.

� CONFIGURATION

This is the name of the configuration. The text analysis feature needs a configu-

ration file. Some standard configurations ship with SAP HANA, and you can also

create your own.

� TEXT MINING OFF

This enables or disables the text-mining feature.


Tip

The full-text index isn’t just used for text searches on a column but can also enable lan-

guage detection, search, text analysis, and text mining on that column.

You open the definition of the column table by double-clicking the table’s name in

the Data Explorer perspective of SAP Web IDE for SAP HANA. In the Indexes tab,

you’ll see the full-text index named DEMO_TEXT_INDEX that you just created.

When you select this index, you’ll see the details of the full-text index as shown in

Figure 10.2.

Figure 10.2 Full-Text Index Details

You can see from the checkboxes that we enabled Fuzzy Search Index and Text Ana-

lysis and disabled Text Mining in this index.

When Fuzzy Search Index is enabled for text-type columns, the full-text index

increases the memory size of the columns by an additional 10% approximately.


When text analysis is enabled, the increase in the memory size due to the full-text

index depends on many factors, but it can be as large as that of the column itself.

Tip

Remember that a full-text index is created for a specific column, not for the entire table.

Figure 10.2 shows the Column Name as SOME_TEXT. You’ll have to create a full-text

index for every column that you want to search or analyze.

SAP HANA will automatically create full-text indexes for columns with data types

TEXT, BINTEXT, and SHORTTEXT. These are called implicit full-text indexes. In this case,

the content of the table column is stored in the form of the full-text index to save

memory consumption. The original column content is recreated from the full-text

index when you use a SQL SELECT statement. Because the focus for TEXT-type fields

is on search, SQL operations such as JOIN, CONCAT, LIKE, HAVING, WHERE, ORDER BY, and

GROUP BY, and aggregates such as COUNT and SUM aren’t supported.

For other column types, you must manually create the full-text index, as we

described in Listing 10.1. You can only manually create a full-text index for col-

umns with the VARCHAR, NVARCHAR, ALPHANUM, CLOB, NCLOB, and BLOB data types. When

you manually create a full-text index, it’s called an explicit full-text index. The con-

tent of the table column is stored in its original format.

When you manually create an index, SAP HANA attaches a hidden column to the

column you indexed. This hidden column contains extracted textual data from

the source column; the original source column remains unchanged. Search que-

ries are performed on the hidden column, but the data returned is from the source

column. The full-text index doesn’t store the results of your search or analysis, but

it does enables the search/analysis.

Note

You can put the SQL statement in a procedure (using an .hdbprocedure file). This way, you

can deploy it later on the production system. The recommended way to create full-text

indexes is using core data services (CDS). You can find the CDS syntax in the SAP HANA

Search Developer Guide at http://s-prs.co/v487620, in the Creating Full-Text Indexes

Using CDS Annotations section.

You can create the same full-text index as you did with SQL code in Listing 10.1

using CDS syntax in an .hdbfulltextindex file. When you compare this with Listing

10.2, you’ll see that they are identical, except that the CDS file doesn’t have the CRE-

ATE statement.



One big difference, however, is that the SQL code uses the built-in configuration

file called EXTRACTION_CORE_VOICEOFCUSTOMER. If you’re using CDS, you’ll

have to make a copy of this configuration and store it in SAP Web IDE for SAP

HANA as an XML file.

FULLTEXT INDEX DEMO_TEXT_INDEX ON"DEMO_TEXT.Text_Data" ("SOME_TEXT")

LANGUAGE DETECTION ('EN')SEARCH ONLY ONFUZZY SEARCH INDEX ONTEXT ANALYSIS ON

CONFIGURATION 'EXTRACTION_CORE_VOICEOFCUSTOMER'TEXT MINING OFF

Listing 10.2 Creating a Separate Full-text Index Using an .hdbfulltextindex File

As you should create all your files using CDS, you can actually create the full-text

index in the same .hdbcds file used for creating the table. The creation of the full-

text index is included as a Technical Configuration, as shown in Listing 10.3.

context DEMO_TEXT3 {Entity Text_Data2 {

key TEXT_ID: Integer not null;SOME_TEXT: hana.VARCHAR(100);

}Technical Configuration {

FULLTEXT INDEX DEMO_TEXT_INDEXON (SOME_TEXT)

LANGUAGE DETECTION ('EN')SEARCH ONLY ONFUZZY SEARCH INDEX ONTEXT ANALYSIS ON

CONFIGURATION 'EXTRACTION_CORE_VOICEOFCUSTOMER'TEXT MINING OFF;

};};

Listing 10.3 Creating a Full-Text Index in an .hdbcds File

In Figure 10.1 earlier, you saw that the creation of the full-text index involved the

following four steps:

� File processing

If the column that you’re creating the full-text index on contains documents,

the file processing step will extract text or HTML from any binary document

format to text/HTML. Some of the supported Multipurpose Internet Mail

Extensions (MIME) types are PDF, Microsoft Office documents, LibreOffice


documents, plain text, HTML, and XML. These documents are typically stored in

a BLOB-type field in the database table.

� Language detection

SAP HANA has the ability to identify the language the text is written in. It then

uses this to apply the appropriate tokenization and stemming in the next steps.

You can specify the language as in the example code of Listing 10.3. You can also

specify a column name that specifies the language for that record. There are 32

languages supported in SAP HANA 2.0 SPS 03. Every new SPS release of SAP

HANA keeps enhancing the list of languages and the available features. Not all

features are available in all languages. Some features are only available for

English, other are available for a few languages, and some are available for all

the available languages.

� Tokenization

This is where the system breaks documents, paragraphs, and sentences into

individual words; for example, “I need some coffee” is decomposed to the

words “I”, “need”, “some”, and “coffee”.

� Stemming

Here the system normalizes tokens to its linguistic base form, for example, the

words “ran”, running”, and “runs” are all converted to the stem word “run”.

The results of these steps are stored in the full-text index and used when you

query the column for text search results.

Text Search

Text searching works on all string columns, even without a full-text index. How-

ever, text search will always use the full-text index if it exists for the preprocessed

text that has gone through file filtering and tokenization. Full-text indexes are

especially beneficial for multiple words and long text, such as documents.

After you’ve created a full-text index, you can do various types of text searches.

The first type of search is the exact text search, which is comparable to a WHERE

clause in SQL, but can be more powerful. This is the default type of text search that

will be used if you don’t specify the text search type explicitly. When you do a lin-

guistic text search, the search will find matches using stemming and return all

words with the same stem as the searched word.

When you’ve created a full-text index with the fuzzy search option enabled, you

can do fault-tolerant searches on your text data. (These are also called fuzzy

searches.) This means you can misspell words, and SAP HANA will still find the text


for you. The real-world scenario at the beginning of the chapter illustrates this

point in a somewhat exaggerated manner. SAP HANA will find alternative (US and

UK) spellings for words such as color/colour, artifact/artefact, utilize/utilise, and

check/cheque. Incorrectly entered words, such as typing coat, will still return the

correct word, which was probably cost.

Note

SAP HANA includes an embedded search via the built-in sys.esh_search() procedure.

With this procedure, you can use an existing ABAP SQL connection instead of an SAP

HANA extended application services, classic model (SAP HANA XS) OData service for fuzzy

text searches in SAP HANA from SAP business applications.

For more information on this new application programming interface (API), see the SAP

HANA Search Developer Guide at http://s-prs.co/v487620.

In Chapter 8, we discussed the LIKE operator in SQL. You can search for a random

pharmaceutical product name (e.g., Guaiphenesin) via the following SQL state-

ment:

SELECT productname, price from Products where productname LIKE

‘%UAIPH%’;

This will find the name if you type the first few characters correctly. If you search

for “%AUIPH%” (which is misspelled) instead, you won’t find anything. There is a

better way that will return the text you’re searching for, in spite of being mis-

spelled.

In SAP HANA, you can use the CONTAINS predicate to use the fuzzy text search fea-

ture. In this case, you use the following SQL statement:

SELECT SCORE(), productname, price from Products whereCONTAINS(productname, ‘AUIPH’, FUZZY(0.8)) ORDER BY SCORE() DESC;

The CONTAINS predicate only works on column tables. It doesn’t work across multi-

ple tables or views and doesn’t work with calculated columns.

The SCORE() function helps sort the results from the most relevant to the least rel-

evant.

Tip

Remember that your users won’t type SQL statements; you’ll create these statements in

a user-defined function (UDF) and call them from your graphical calculation view or your

SAP HANA extended application services, advanced model (SAP HANA XSA) application.



The CONTAINS predicate has three parameters: the column name you want to search

on, the search string itself (even if misspelled), and how “fuzzy” you want the

search results to be in this case. The fuzziness factor can range from zero (every-

thing matches) to one (requires an exact match). This factor is called the fuzzy

threshold value.

Note

There are two terms used for the fuzziness factor. When you specify the amount of fuzzi-

ness in the query, it’s called the fuzzy score. When you get the results, the fuzziness factor

is called the fuzzy threshold.

Using the FUZZY() function as the third parameter of the CONTAINS predicate shows

that you want to do a fuzzy text search. If you want to do an exact text search,

instead use the EXACT keyword or leave out the third parameter. You can use the

LINGUISTIC keyword as the third parameter to do a linguistic text search.

Tip

The linguistic text search uses the language detection and stemming results from the

full-text index. It’s quite a lot of work for the system to create the stemming results, so

SAP HANA allows you to switch it off when you create the full-text index by specifying

FAST PREPROCESS OFF.

If you want to search on all columns with a full-text search index, you can specify

the CONTAINS predicate as follows:

CONTAINS (*, ‘AUIPH’, FUZZY(0.8))

In this case, the asterisk (*) in the first parameter to the CONTAINS predicate ensures

that the search includes all columns. When you search on multiple columns at

once, it’s called a freestyle search. Instead of a freestyle search, you can specify

which columns (multiple) you want to include or exclude in the list.

You can use some special characters in the search string (the second parameter) as

well:

� Double quotes (")

Everything between the double quotes is searched for exactly as it appears.

� Asterisk (*)

Replaces zero or more characters in the search string; for example, cat* matches

cats, catalogues, and so on.


� Question mark (?)

Replaces a single character in the search string; for example, cat? matches cats

but not catalogue.

� Minus (-)

When placed in front of a search word, it acts as a NOT statement. The results will

exclude any result where this word occurs; for example, dog* -cat* matches dogs

but not cats and dogs.

You can use AND and OR logic with the search terms, but brackets aren’t supported

as search operators.

SPS 04

In SAP HANA 1.0, you could do a fuzzy text search in an attribute view. Attribute views

were deprecated many years ago, and the ability to do a fuzzy text search in a graphical

calculation view wasn’t implemented until SAP HANA 2.0 SPS 04. It’s quite powerful, as it

can be implemented with all calculation views.

Figure 10.3 shows the Semantic node of a calculation view. The Parameters dropdown

menu has two new options added. We discussed the new Session Variable in Chapter 7.

The Fuzzy Search Parameter allows you to create a fuzzy text search for your graphical

calculation view. You can specify the Fuzzy Score, as well as the Default Search String.

As with other Parameters, such as Input parameter, you can then assign it to an output

field.

Figure 10.3 Using Fuzzy Text Search in a Calculation View with SAP HANA 2.0 SPS 04


You can combine text search queries with additional functions to enhance your

text search queries and results. With the SCORE() function, you’ve just seen an

example of an additional function that helps with sorting the results of the text

search by relevance. Some other functions that you can use are as follows:

� NEAR()

This function allows you to do a proximity search for words that are near to each

other. Let’s say you have a sentence like “I passed the SAP HANA certification

exam.” The query WHERE CONTAINS(*, ‘NEAR(exam, hana), 2, false’) will find the

places where the word exam is within 2 tokens distance of the word HANA. The

false parameter indicates that you don’t care about the sequence of the words.

� HIGHLIGHTED()

You can use this function to highlight the search term in the results in bold.

Your query can be something like SELECT HIGHLIGHTED(TITLE), DESCRIPTION FROM

TABLE1 WHERE CONTAINS(“TITLE”), ‘HANA’);. The search term “HANA” will be

shown in bold; for example, I passed the SAP HANA certification exam.

� SNIPPETS()

This function is similar to the HIGHLIGHTED() function, except that it will return a

short snippet instead of the entire sentence or paragraph. For example, a query

like SELECT SNIPPET(TITLE), DESCRIPTION FROM TABLE1 WHERE CONTAINS(“TITLE”),

‘HANA’); will return . . . passed the SAP HANA certification . . . with an ellipsis on

both sides of the snippet.

� WEIGHT()

With this function, you can give a higher relevance (more weight) to search

terms that are in the Title column than those in the Description column—using

the table in Figure 10.1 as an example. You can create a query like WHERE CON-

TAINS((“TITLE”, “DECRIPTION”), ‘exam’, WEIGHT(1, 0.5)). The weight is a value

between 0 and 1. In this case, the query will give a relevance of 1 for finding the

word exam in the Title and 0.5 for finding it in the Description.

Text Analysis

After you’ve created a full-text index with the TEXT ANALYSIS option enabled, you

can also analyze text with the SAP HANA text analysis feature, both via entity

analysis and sentiment analysis.

You create text analysis via the full-text index. The abbreviated statement is as fol-

lows:


CREATE FULLTEXT INDEX INDEX_NAME ON TABLE_NAME ("COLUMN_NAME")TEXT ANALYSIS ON

CONFIGURATION 'CONFIG_NAME';

You need the TEXT ANALYSIS ON clause, and a CONFIGURATION NAME is optional. There

are a few built-in configurations, but you can also create your own industry-spe-

cific configuration files. Each of the configurations has its own unique extraction

rules and dictionaries of entity values and types.

In the earlier example shown in Listing 10.1, we created the full-text index (called

DEMO_TEXT_INDEX) on table DEMO_TEXT. When you look at the table in the SAP HANA

database explorer perspective, you’ll notice that SAP HANA has created an addi-

tional table: $TA_DEMO_TEXT_INDEX (see Figure 10.4, bottom left). This is the same

name of the full-text index we created, prepended with $TA_ to denote text analy-

sis. You get the text analysis request results back in table $TA.

Tip

Table $TA isn’t the full-text index; it’s a table that contains the results of the text analysis.

Figure 10.4 Source Table (DEMO_TEXT) and $TA_ Text Analysis Results Table

The original table included the values in Table 10.1 in the SOME_TEXT column.


Figure 10.5 shows the text analysis request results, using the EXTRACTION_CORE_VOI-

CEOFCUSTOMER configuration file that is used for sentiment analysis. The text analy-

sis determined the following points:

� Laura, Colin, and Danie are people.

� SAP is classified as an organization.

� Apples, chocolate, and running are topics.

� Laura liking apples is a weak positive sentiment.

� Laura disliking chocolate is a weak negative sentiment.

� Colin loving running is a strong positive sentiment.

With a different text analysis configuration file, we’ll receive different results, as

shown in Figure 10.6.

Figure 10.5 $TA_ Text Analysis Results Table Content

Laura likes apples. Laura dislikes chocolate.

Colin loves running.

Danie works for SAP.

Table 10.1 SOME_TEXT Column


Using the standard configuration files, you’ll be able to identify many of the attri-

butes (master data) in your text data. Identifying measures isn’t always that easy.

For example, is 2020 a year, an amount, or a quantity?

Figure 10.6 shows the $TA results table when no configuration was specified and

with the FAST PREPROCESS OFF; allowing the text analysis to also do stemming. The

TA_TOKEN column shows the various words (tokens), and the TA_TYPE column

lists the linguistic or semantic type of that token. The TA_STEM column contains

the stem of that token; for example, the stem of running is run, and the stem of

apples is apple.

Figure 10.6 The $TA_ Text Analysis Results Table for Stemming and Entity Extraction

Tip

The text analysis results are stored in a normal SAP HANA table. As such, you can use the

results in many scenarios such as calculation views, SAP HANA XSA applications, and data

mining tools.

The optional Grammatical Role Analysis (GRA) analyzer for English is also avail-

able. It identifies groups of three in the form of subject–verb–object expressions.

For an example, let’s look at a well-known phrase:

The [SUBJECT]quick brown fox[/SUBJECT] [VERB]jumps[/VERB]over the [DIRECTOBJECT]lazy dog[/DIRECTOBJECT]

The GRA analyzer identifies the fox and the dog as objects, and it identifies jumps as

the verb.


Tip

Text analysis results are stored in a flat structure with the table $TA. As such, it’s hard to

represent the complex relationships between the extracted entities. This is one area

where graph processing can certainly help. By combining text processing with graph

analysis, you can start using graph queries from Chapter 11 to examine the text analysis

with various algorithms. For example, you can discover closely connected relationships

between entities that you won’t see when just looking at table $TA.

SAP HANA has seven built-in text analysis configurations. You’ve already seen the

EXTRACTION_CORE_VOICEOFCUSTOMER configuration when we used it for sentiment

analysis. There are three configurations for linguistic analysis.

� LINGANALYSIS_BASIC

This configuration is used for tokenization only.

� LINGANALYSIS_FULL

This configuration is used for tokenization and stemming. If you don’t specify a

configuration file, this configuration is used. An example of the results was

shown earlier in Figure 10.6.

� LINGANALYSIS_STEMS

This configuration is used for tokenization, stemming, and parts of speech.

Parts-of-speech tags word categories, for example, showing that quick is an

adjective.

The four configurations for extraction analysis extend the LINGANALYSIS_FULL con-

figuration:

� EXTRACTION_CORE

This configuration is used for extracting standard entities, such as persons and

phone numbers.

� EXTRACTION_CORE_ENTERPRISE

This configuration is used for extracting business relations. Examples are mem-

bership information, management changes, product releases, mergers and

acquisitions, and organizational information.

� EXTRACTION_CORE_PUBLIC_SECTOR

This configuration is used for extracting government entities. Examples are

action and travel events, military units, spatial references, and details of per-

sons, such as aliases, appearances, attributes, and relationships.

� EXTRACTION_CORE_VOICEOFCUSTOMER

This configuration is used for extracting sentiment analysis. We saw an exam-

ple of this when we produced the results in Figure 10.5.


All text analysis configurations consist of three elements.

� Dictionary

The dictionary is a repository of entity categories and their possible values, for

example, top soccer (football) countries such as Spain, Germany, Netherlands,

Italy, and Brazil. It’s created with an .hdbtextdict file.

� Rule set

The rule set contains the rules that define how an entity can be identified; for

example, a national identity number can start with the birth date of the person

and should be 14 numbers in total. It’s created with an .hdbruleset file.

� Text analysis configurations file

The configuration file defines aspects of the text analysis process, for example,

which dictionaries and rule sets to use, whether to use linguistic or entity

extraction, and which phases of these extractions (like tokenization) should be

used. It’s created with an .hdbtextconfig file.

When you build your own custom text analysis configuration, you’ll build the files

in the sequence above. It might be easier, however, to copy the existing files in SAP

HANA and then modify them. All three of these files are XML files that can be

edited with standard editors, including the code editor in SAP Web IDE for SAP

HANA.

Note

For more information about text analysis configurations, refer to the SAP HANA Text

Analysis Developer Guide at http://s-prs.co/v487620.

Text Mining

Fuzzy searches and text analysis work with a word, sentence, or paragraph—but

with text mining, you can work with entire documents. These documents can be

web pages, Microsoft Word documents, or PDF files that are loaded into SAP

HANA.

Text mining uses the full-text indexing and text analysis processes, as shown ear-

lier in Figure 10.1. Text mining uses these to produce a matrix of documents and

terms. When using text mining in SAP HANA, you can perform the following tasks:

� Categorize documents.

� Find top-ranked documents related to a particular term.



� Analyze a document to find key phrases that describe the document.

� View a list of documents related to the one you asked about.

� Access documents that best match your search terms.

When you activate text mining when creating the full-text index, it also creates a

few $TM tables. The term-document matrix (known as the text mining index) is an

optional data structure that uses the results of text analysis.

Figure 10.7 shows the text mining results for the data in Table 10.1 that we previ-

ously used for the text analysis. It’s called a term-document matrix, as the terms

occurring in each document are listed with their frequency. The TEXT_ID column

has the number of the document, which, in this case, was just one or two sen-

tences.

This is a very small example. For a large corpus of documents, this can become a

large data set. You can apply powerful mathematical, statistical, and data mining

processes to this data set.

Figure 10.7 The $TM Text Mining Results Tables and the Contents of the Text Mining Index

SAP HANA also provides several text mining functions to help process the results

table. There are functions to find related terms, suggested terms, relevant terms,

relevant documents, related documents, and document categories.

The document categories are created using the on k-nearest neighbor (“kNN” or

“k-NN”) algorithm. You’ll see it mentioned again in the next section on predictive

modeling. You can call the text mining functions from SQL functions or from web

applications.


Tip

Remember that users won’t work at the lower level that we’ve described in this chapter:

They will want to use text features from a browser-based application interface. This is

why SAP has focused on supplying various JavaScript APIs for SAP HANA modelers and

developers.

Note

You can get more information in the “Full-Text Search with SAP HANA Platform” open-

SAP course at http://s-prs.co/v487632.

At http://s-prs.co/v487620, you can find three developer guides for text processing: the

SAP HANA Search Developer Guide, the SAP HANA Text Analysis Developer Guide, and

the SAP HANA Text Mining Developer Guide. You will have to click on the View All button

in the Development section to find some of these guides. Or download all the files from

the Documentation Download section.

You can also view the SAP HANA Academy on YouTube. The playlist for text processing is

at http://s-prs.co/v487633.

Now that you know how to work with text data in SAP HANA and can use fault-tol-

erant text searches, text analysis, and text mining in SAP HANA modeling work,

let’s move on to predictive modeling.

Predictive Modeling

In this section, we’ll look at the SAP HANA Predictive Analysis Library (PAL) and

how to use it to create a predictive analysis model. We’ll look at predicting future

probabilities and trends and how you can use this functionality in business. In the

real-world scenario at the beginning of the chapter, we described a use case in

which you might create a predictive model in SAP HANA to help sales reps with

their sales forecasting by looking at seasonal patterns; for example, people use

more pharmaceuticals for allergies in the spring.

There are many use cases for PAL, which is built in to SAP HANA. Here are just a

couple examples:

� For financial institutions such as banks, you can detect fraud by looking at

abnormal transactions.

� For fleet management and car rental companies, you can predict which parts

will break soon in a vehicle and replace them before they actually break.


http://s-prs.co/v487620,



We’ll see some more use cases when we discuss the types of algorithms and what

you can use them for. Before discussing PAL in more detail, let’s take a quick look

at the wider picture. PAL is just one of several tools available for addressing

machine learning and predictive analysis requirements in your projects.

� Predictive Analysis Library (PAL)

PAL provides more than 90 classic and trending algorithms for native in-data-

base processing of data that you can use in your information modeling.

� Automated Predictive Library (APL)

The embedded SAP Predictive Analytics automated analytics engine processes

your data automatically with multiple scenarios and determines which predic-

tive algorithm is the best for your scenario using structural risk minimization.

APL makes it quick and easy for nonexpert data scientists to consume applica-

tions built on SAP HANA.

� SAP domain-specific libraries

There are several SAP solutions that have libraries for native in-database pro-

cessing of application data, for example, the Unified Demand Forecast library

for customer activities and the Optimization Function Library (OFL) for supply

chain solutions.

� Application Function Library (AFL) Software Development Kit (SDK)

This SDK allows SAP partners and customers to create their own functions in

libraries similar to PAL, OFL, and Unified Demand Forecast. The code is written

in C++, and the custom-created libraries can be used internally in the company

or can be sold to SAP customers.

� TensorFlow integration

TensorFlow is an open-source deep learning library from Google that is used for

many scenarios, for example, image classification where the system “recog-

nizes” objects in photographs. You can call TensorFlow models from SAP HANA

External Machine Learning AFL.

� R integration

You’ve seen in Chapter 8 that you can write procedures using SQLScript. You

can also write procedures in R, AFL, and GraphScript. R is a programming lan-

guage used by many statisticians and data miners. You can do everything in

PAL and more using R. The advantage is that you can reuse lots of code from

other projects. PAL is much faster for business data, however, because it runs in

database, whereas the R code and the data are sent to an R server to run exter-

nally.


SPS 04

Integration to Python was added with SAP HANA 2.0 SPS 04 via a Python API for SAP

HANA machine learning. Many data scientists are using Python for fast prototyping of

their machine learning models. You can now leverage the SAP HANA machine learning

libraries to process the data in SAP HANA from calls in Python.

The R integration has also been expanded with an R API for SAP HANA machine learning

to leverage SAP HANA directly from your R code.

Predictive modeling uses statistical algorithms on a set of input data to “guess” the

probability of a specific outcome. We can use this for data mining where you use

these algorithms to find patterns in the data.

A variation of this that is discussed a lot in the media is machine learning. In this

case, we use statistical algorithms to let the computer look at, analyze, and “learn”

from the data by automatically building a predictive model. We can then run new

data through the generated model to let the system predict and provide the prob-

ability of any outcomes we’re interested in.

We’ll now discuss PAL in more detail.

Predictive Analysis Library

Predictive analytics refers to statistics applied to prediction, machine learning, or

data mining processes. Those statistics need a lot of complex algorithms. You can

program these algorithms in a programming language such as Java or use algo-

rithms already created in a language such as R. The problem is that you then have

to transfer the data to external servers instead of processing it in SAP HANA. Some

languages, such as Python, are quite slow in general. You should preferably write

such complex algorithms in a compiled language, such as C++, for fast execution.

If you want to keep the data in the database, SQL or SQLScript isn’t an option as

these are set-oriented data languages.

PAL is a library of such algorithms, written in C++, and running inside SAP HANA.

You don’t have to transfer any data between SAP HANA and another application

server.

The algorithms in PAL were carefully selected based on the most commonly used

algorithms in the market, algorithms generally available in other database prod-

ucts, and algorithms required by SAP applications. Over the years, the library has

grown to fulfill almost all requirements from SAP HANA projects. Recently added

algorithms tend to be the popular trending algorithms from areas such as

machine learning.


In SAP HANA, the predictive analysis functions are grouped together in the PAL.

PAL defines functions that you can call from SQLScript and use to perform various

predictive analysis algorithms.

You can use these PAL functions to prepare your data for further analysis, for

example, building a predictive model. You can then use this trained model on new

data to make predictions for this data. For example, when traveling, you want to

know if your flight will be delayed. You can train a predictive model by feeding it

all the historical flight data. After the model is prepared, you can give it your flight

details. The model will then predict whether your flight will be delayed or not.

You’ll learn how to build this in SAP HANA in the “Machine Learning Models” sec-

tion later in this chapter.

In PAL, predictive analysis algorithms are grouped in 10 categories; we’ll take a

quick look at some of the categories and discuss a few popular algorithms to give

you an idea of what is possible. The categories, minus the miscellaneous one that

serves as a catch-all for anything left over, are as follows:

� Association

For an example of an association use case, think of Amazon’s suggestions: Cus-

tomers who bought this item also bought. The Apriori algorithm can be used to

analyze shopping baskets. It can then identify products that are commonly pur-

chased together and group them together in the shop, such as flashlights and

batteries. You could even offer a discount on the flashlights because you know

that people will need to buy batteries for them, and you can make enough profit

on the batteries. (Notice, however, that the inverse isn’t necessarily true; a dis-

count on the batteries won’t encourage people to buy flashlights.)

� Clustering

With clustering, you divide data into groups (clusters) of items that have similar

properties. For example, you can group servers with similar usage characteris-

tics, such as email servers that all have a lot of network traffic on port 25 during

the day. You might use the popular k-means algorithm for this purpose.

Another example is computer vision, in which you cluster pictures of human

faces using the affinity propagation algorithm. Clustering is sometimes also

called segmentation. The focus of clustering is the algorithm figuring out by

itself what is the best way to group existing data together.

� Regression

Regression plots the best possible line or curve through the historical data. You

can then use that line or curve to predict future values. This could be used by the

previously mentioned car rental company. The company has had 5,000 of these


vehicles before, and so it has good information available to predict when certain

parts in a similar vehicle will break.

� Classification

The process of classification takes historical data and trains predictive models

using, for example, decision trees. You can then send new data to the predictive

model you’ve trained, and the model will attempt to predict which class the new

data belongs to. This process is often referred to as supervised learning. The

focus here is on getting accurate predictions for unknown data.

� Preprocessing and data preparation

This category of algorithms is used to prepare data before further processing.

These algorithms may improve the accuracy of the predictive models by reduc-

ing the noise.

� Time series

For an example of a time series, think of the real-life scenario at the beginning

of the chapter, in which we built a predictive model to forecast sales by looking

at seasonal patterns. You might also see, for example, a monthly pattern in

shops of higher sales after paydays.

� Social network analysis

Algorithms in this category are used to predict the likelihood of a future link

between two people who have no link yet. This is used by companies such as

Facebook and LinkedIn.

� Statistics and outlier detection

This category includes some statistical algorithms to find out how closely vari-

ables are related to or are independent from each other. One example is the

Grubbs’ test algorithm, which is used to detect outliers. This could be used in the

fraud detection use case we mentioned earlier, in which certain transactions are

different from the others. (Often, we first use a clustering algorithm to group

transactions together and then detect outliers for each group/cluster after.)

� Recommender systems

These algorithms, as the name suggests, make recommendations by analyzing

product associations, the purchase history of the buyer, and external factors,

such as the general economy. Music playlists are good examples.

Figure 10.8 shows the PAL algorithms, per category, that are available in SAP HANA

2.0 SPS 03.


Figure 10.8 Predictive Functions Available in SAP HANA 2.0 SPS 03, Grouped by Category

SPS 04

New algorithms, such as the trending and popular Hybrid Gradient Boosting Tree algo-

rithm for classification and regression, were added in SAP HANA 2.0 SPS 04. Another algo-

rithm included is Change Point Detection, which is used for detecting abrupt changes in

time-series data such as stock prices. Other examples are Geometry DBSCAN (which we’ll

meet again in the geospatial processing in the next chapter), Missing Value Handling,

Entropy, and Discretize.

Performance of all the PAL function categories have been improved. A useful new func-

tion is the Regression Model Comparison function, which allows you to run your data

across many different regression algorithms in PAL using a single function call, and then

compare the results. Error messages and logging are also now more detailed, which helps

with debugging.

Our discussions about the PAL algorithms focuses on how to use them with SAP

HANA. We can’t discuss when to use which algorithm, as this is a specialist field on

Statistics

ANOVA

Chi-Squared Goodness-of-Fit Test

Chi-Squared Test of Independence

Condition Index

Covariance Matrix

Cumulative Distribution Function

Distribution Fitting/Weibull analysis

Distribution Quantile, Quantile Function

F-test (Equal Variance Test)

Factor Analysis

Grubbs’ (Outlier) Test

Kaplan-Meier Survival Analysis

Mean, Median, Variance, Standard Deviation, Kurtosis, Skewness

One-Sample Median Test

Pearson Correlations Matrix

T-TestUnivariate Analysis/Multivariate Analysis

Wilcox Signed Rank Test

Clustering

ABC Classification

Accelerated K-Means/K-Means

Affinity Propagation

Agglomerate Hierarchical Clustering

Cluster Assignment

DBSCAN

Gaussian Mixture Model (GMM)

K-Medians

K-Medoids

Latent Dirichlet Allocation (LDA)

(Kohonen) Self-Organizing Maps

Classification

Area Under Curve (AUC)

Back-Propagation (Neural Network)

C4.5 Decision Tree

CART Decision Tree

CHAID Decision Tree

Confusion Matrix

Gradient Boosting Decision Tree (GBDT)

KNN (K Nearest Neighbour)

Linear Discriminant Analysis (LDA)

Logistic Regression (with Elastic Net Regularization)

Multi-Class Logistic Regression

Multilayer Perceptron

Naïve Bayes

Random Decision Trees/Forest

Support Vector Machine

Regression

Bi-Variate Geometric, Bi-Variate Natural Logarithmic Regression

Cox Proportional Hazard Model

Exponential Regression

Generalized Linear Models (GLM)

Multiple Linear Regression

Polynomial Regression

Association

Apriori, AprioriLite

FP-Growth

K-Optimal Rule Discovery (KORD)

Sequential Pattern Mining

Preprocessing

Anomaly Detection

Binning, Binning Assignment

Inter-Quartile Range (Tukey’s Test)

Multidimensional Scaling (MDS)

Partition

Principal Component Analysis (PCA)

Random Distribution Sampling

Sampling

Scale, Scale with Model

Substitute Missing Values

Variance Test

Time Series

ARIMA/Auto-ARIMA

Auto/Brown/Single/Double/ Triple Exponential Smoothing

Correlation Function

Croston's Method

Fast Fourier Transform (FFT)

Forecast Accuracy Measures

Forecast Smoothing

Hierarchical Forecast

Linear Regression with Damped Trend and Seasonal Adjust

Seasonality Test, Trend Test, White Noise Test

Social Network Analysis

Link Prediction – Common Neighbors, Jaccard’s Coefficient,

Adamic/Adar, Katzβ

PageRank

Recommender Systems

Alternating Least Squares

Factorized Polynomial Regression Models

Field-Aware Factorization Machines (FFM)

Miscellaneous

ABC Analysis

Weighted Score Table


its own. To properly use PAL in SAP HANA, you’ll either need to know what and

when to use it yourself or work with someone who does.

Note

If you want to learn about the different PAL algorithms in more detail, there are two great

sources. The first is the SAP HANA Academy on YouTube, which has a dedicated video for

each algorithm and a few overview video tutorials. You can find the PAL playlist at http://

s-prs.co/v487634.

The other source is the SAP HANA Predictive Analysis Library (PAL) Reference Guide from

the Reference section at http://s-prs.co/v487620. You can start with the table of contents

to look at the categories and see which algorithms are available in the category you need.

You can then look at the information for each of the algorithms, for example:

� The description of the algorithm, its perquisites, and capabilities

� The inputs and outputs that are required, as well as the data types for each column

� Sample code for using the algorithm, and what results can be expected

If you already know the specific algorithm that you want to use or are working

with someone who does, you can use the search feature to find that specific func-

tion.

Installing and Configuring Predictive Analysis Library

PAL is an optional installation with SAP HANA. As it’s part of the SAP HANA, enter-

prise edition, you might have to ask the SAP HANA system administrator to install

it for your project. If you’re using the SAP HANA, express edition, as we are in this

book, it’s already preinstalled for you when you use the Server + Applications ver-

sion. You’ll have used this version if you followed along when we discussed the

basic SAP HANA modeling in SAP Web IDE for SAP HANA.

PAL is part of the AFL that ships with all SAP HANA systems. AFL is an optional

component; if you want to use the predictive analysis algorithms in PAL, you have

to install AFL. AFL also includes a Business Function Library (BFL). You can check

that PAL is installed by querying the system views SYS.AFL_AREAS and SYS.AFL_

FUNCTIONS.

PAL uses a special engine in SAP HANA called the scriptserver. Because partners

and customers can write their own function libraries using the AFL SDK, and

because some algorithms can run for a longer time, SAP HANA has to protect its

kernel against any instability. PAL and any other function libraries written with

the AFL SDK run inside the scriptserver. By default, the scriptserver isn’t running.





You’ll need to add the scriptserver in your tenant database. In the SAP HANA,

express edition, this is named HXE. The scriptserver doesn’t run in the SYSTEMDB.

You can run the following command while you’re connected to the SYSTEMDB data-

base (replace HXE with the name of your tenant database):

ALTER DATABASE HXE ADD 'scriptserver';

After the installation, you have to ensure that the scriptserver is started in the

tenant database and make sure the correct roles and authorizations are assigned

to your SAP HANA user. (Roles and how to assign them are discussed in Chapter

13.)

You can check that the scriptserver is running, by running the following query in

the tenant database:

SELECT * FROM SYS.M_SERVICES;

You also have to assign the necessary roles and privileges. Your development user

will need the AFL__SYS_AFL_AFLPAL_EXECUTE role to execute the PAL procedures.

Both the SAP HANA Deployment Infrastructure (HDI) service technical user and

the HDI container owners need to have been granted SELECT privileges on the PAL

procedures. The HDI service technical user can be granted privileges through its

specific default role called XSA_DEV_USER_ROLE. You’ll need to assign this role to your

development user as well.

The HDI container owners can be granted privileges through the default role called

_SYS_DI_OO_DEFAULTS.

The easiest way to set this up properly is to use the code snippet provided by the

SAP HANA Academy. You can view the video called “PAL 146 Getting Started with

HANA 20 SPS02” in the YouTube playlist we mentioned in the previous subsec-

tion. Or you can just run the code snippet on GitHub that Phillip describes in the

video (the code snippet for setting this up is available at http://s-prs.co/v487635).

You’ll have to do this for your SAP HANA, express edition, or you won’t be able to

create the flowgraphs we’ll discuss next.

After PAL is installed and configured, you can create predictive models graphically

in SAP Web IDE for SAP HANA. There are three ways to call the PAL algorithms.

� The easiest way to is to create a flowgraph. Even people who don’t know SAP

HANA modeling that well, such as a data scientist in your team, can use the

flowgraphs.



� You can also create an SAP HANA AFLLANG procedure. We won’t go into much

detail on this.

� You can call the catalog SQLScript procedures provided.

We’ll start using PAL by creating flowgraphs.

Creating a Predictive Model Using Flowgraphs

We mentioned flowgraphs in Chapter 9 already, when discussing SAP HANA smart

data integration and SAP HANA smart data quality. We’ll configure the predictive

analytics flowgraphs to generate the procedures we need to execute the predictive

model. We can, however, also run the model directly inside the flowgraph editor. A

flowgraph is a file with an .hdbflowgraph extension. It allows you to design and run

data transformation procedures and tasks without typing a single line of code.

You create an .hdbflowgraph file from the New context menu in your project. The

flowgraph editor opens, as shown in Figure 10.9. Before creating your flowgraph,

you’ll need to set the runtime behavior type of your flowgraph. You can do this in

the Properties dialog box. You can open this dialog box by clicking on the top-right

icon that looks like a small gear. In the Settings tab, you can select the Runtime

Behavior Type in the dropdown menu. There are four options:

� Batch Task

This behavior type processes data as a batch or when loaded with an initial load.

It can’t be run in real time. A stored procedure and a task are created after build-

ing the flowgraph. You can schedule this to run at regular intervals.

� Realtime Task

This behavior type allows you to process data in real time. A stored procedure

and two tasks are created when you build the flowgraph. The first task is a batch

or initial load of the input data. The second task is run in real time for any

updates that occur to that input data.

� Transactional Task

This also allows you to process data in real time. A single task is created after

building the flowgraph. This task is run in real time when any updates occur to

the input data. It doesn’t include an initial load as it assumes that all data col-

lected comes from a transactional system.

� Procedure

We’ll only discuss this type of runtime behavior in this chapter. It allows you to

process data using stored procedures. It can’t be run in real time. When you


build the flowgraph, it will automatically create the stored procedures for you.

You can then run the flowgraph when you want to. This works best when ana-

lyzing data.

Figure 10.9 Setting the Properties of the Flowgraph

In Figure 10.10, we build a graphical flowgraph model. Start by clicking on the large

+ sign. A palette menu will pop up for you to select nodes to use in your flowgraph

model. In this case, we select the Predictive Analysis node.

In the case of a Predictive Analysis node, another dialog box will pop up where you

chose your algorithm. The PAL functions are grouped in the categories that we dis-

cussed earlier. You can expand each category to see the algorithms available in

that category.

Note

One thing to keep in mind is that the graphical flowgraph editor is typically one release

behind the PAL. If you really need one of the latest PAL algorithms, you’ll have to call it

with SQLScript.


Figure 10.10 Creating a Flowgraph Model in SAP Web IDE for SAP HANA

Your flowgraph must consist of at least three nodes, as shown in Figure 10.11. You

need a Data Source node from which the data is fed into the Predictive Analysis

node, where it’s processing depends on the algorithm you chose. This is also some-

times called the Transformation node. The output from the predictive analysis is

then sent to the Data Target node.

Figure 10.11 Flowgraph Sequence from Data Source to Data Target

We’ll start with the Predictive Analysis node as the data inputs and outputs are

determined by the PAL algorithm that you choose. In Figure 10.12, we selected the

Accelerated K-Means clustering algorithm. You can see from the title of the algo-

rithm that it expects two inputs and provides two outputs.


You can open the bottom part of Figure 10.12 by clicking on the gear icon of the Pre-

dictive Analysis node. The text description of the algorithm in this case shows that

it expects an ID field and one other column with the data type of integer or double,

and that NULL values aren’t allowed. This is necessary information that we need

when creating or selecting the Data Source node.

Figure 10.12 Configuring the Transformation Node

The Regex column shows [ISF8_19_0] and [IDS] for the different Inputs. [ISF8_19_0]

means that this entry must be a unique identifier. These can be either integers or

strings. In this case, the text description specified that it should be an integer. [ID]

means that it should either be an integer or a double. [IDS] means that several val-

ues can be sent through. In that case, the Multi Column box is selected.


The mapping of input fields from the data source is normally done automatically.

The columns are handled in the same order in which they are defined in the data

source.

You now know from the Predictive Analysis node that the data source must supply

at least two columns, of which the first must be an ID field. These field data types

can either be integer or double.

Figure 10.13 shows the configuration of the Data Source node. In Figure 10.11,

shown earlier, the top-right corner of the Data Source node has a red dot. This indi-

cates that the node isn’t yet configured. To configure it, you click on the gear icon

at the bottom of the node.

The main selection here is the type of data source that you want to use. The HANA

Object button allows you to select SAP HANA tables, views, synonyms, CDS enti-

ties, table functions, and so on. If you don’t use procedure-type flowgraphs, you

can also select calculation views. In this case, we’re using a procedure-type flow-

graph, so we can’t use a calculation view as a data source.

Sometimes you might want to have a more dynamic setup. You can choose to use

a Table Type as a data source, as shown in Figure 10.13. You’ll have to specify the col-

umns and the data type of each column. The input table will be passed into the

procedure call as an input parameter.

Tip

Choosing a Table Type as a data source is only available for procedure-type flowgraphs.

We provide the expected input fields with the required data types to the Predictive

Analysis node. In this example it could be something like product sales data, where

we want to cluster according to quantities. We pass through the item IDs, the

quantities, and the item names.

The configuration of the Data Target node is very similar to that of the Data Source

node. The data target defines where you want to put the calculated results. This

could be an existing SAP HANA table; in which case, you select HANA Object. If

you’re creating a procedure-type flowgraph, you can select Table Type.

If you want to create a new table for the results, you can select Template Table, as

shown in Figure 10.14. If the Predictive Analysis node creates several result sets, you

don’t have to retrieve all of them. If result sets aren’t mapped to a Data Target

node, they won’t be stored.


Figure 10.13 Configuring the Data Source Node

Figure 10.14 Configuring the Data Target Node


You now know how to create simple predictive models. Now let’s discuss how to

create machine learning models.

Machine Learning Models

With machine learning models, we use the predictive algorithms to let the com-

puter look at, analyze, and “learn” from the data by automatically building a pre-

dictive model. We can then run new data through the generated model to let the

system predict the results based on what it has learned from the historical data.

Sometimes, you might want to train your model, and then use it later to make pre-

dictions. Figure 10.15 shows such a model: It starts with some training data that

you feed into a C4.5 decision tree. The data is used to train the classification model.

You then feed the trained classification model into a prediction function and give

the prediction function some new data. The prediction function applies the

trained model to the new data and provides the predicted results in an output

table. This is an example of how you would build a model for predicting delayed

flights. You can train a predictive model by feeding it all the historical flight data.

After the model is prepared, you can give it your flight details. The model will then

predict whether your flight will be delayed or not and by how many minutes. You

can build this exact model using the flights data from the US Department of Trans-

portation that we discussed in Chapter 2.

Figure 10.15 Predictive Model Created in SAP Web IDE for SAP HANA

There is a nice way to check how well your trained model will perform. You use

90% or 95% of the historical data to train your machine learning model. You then


feed the remaining 10% or 5% of the data into your model to get the predicted val-

ues. Because this is actually historical data, you have the result of what really hap-

pened. You can now compare the historical result with the predictive values to

evaluate the accuracy of your predictive model.

The main difference between this model and the simpler one in the previous sec-

tion is that these models have several steps. The first step trains the model, then

you can validate and test it. Finally, you use the trained model for predictions.

You can even add another step before the model training step, by first using parti-

tioning-type algorithms to divide the data into several subsets. You can use 90% of

your data to train the model, and the remaining 10% to validate it with. Or you can

use preprocessing (data preparation) algorithms on the raw data to first shape it

into a usable format.

SPS 04

To improve the performance of machine learning models in SAP HANA 2.0 SPS 04, you

can now keep your trained PAL predictive models in memory. You parse the model once,

keep the model state in memory, and can then execute repeatedly on the stored model

that is kept in memory for repeated scoring.

When you build a flowgraph model, you start on the left side and work toward the

right side, as shown in Figure 10.15. First, add a data input node, and select a table

with data. Put this on the left side of the work area. Next, select a PAL function

from the popup palette menu, and add it to the work area. Then, connect the Data

Source node to the PAL function node. You carry on in this fashion until your data

model is complete.

When you want to run your predictive model, you can click the Execute button in

the top left of the flowgraph editor. The biggest advantage of flowgraphs is that

they are very easy and flexible. Data science can be quite complex, and you don’t

always know which algorithms will produce the best results. Using graphical flow-

graphs, you can test the performance of several algorithms to find the best solu-

tion.

SQLScript Procedures Created by the Flowgraph

When you’re satisfied with your predictive model, you can build it in SAP Web IDE

for SAP HANA. With a procedure-type flowgraph, it will create a set of two proce-

dures automatically during the build process.


You can find these two procedures by going to the Procedures folder in the SAP

HANA database explorer, as shown in Figure 10.16. The SAP HANA database

explorer is accessed via the second icon on the top-left toolbar of SAP Web IDE for

SAP HANA. In this case, we used the simple predictive model created earlier in

Figure 10.11, using the accelerated k-means algorithm. The flowgraph was called

DEMO_INTRO.hdbflowgraph.

The two procedures are seen on the list of procedures on the left side: DEMO_

INTRO and DEMO_INTRO.PredictiveAnalysis. The last part of the name is due to the

naming of the Transformation node in Figure 10.11.

When you open the procedures, you can see the code in the CREATE Statement tab.

Figure 10.16 shows the code for the first procedure, called DEMO_INTRO.

Figure 10.16 SQLScript Procedure Created by the Flowgraph

The first procedure is a SQLScript procedure and is shown in Listing 10.4. After the

BEGIN statement, it first calls a parameters table that was also automatically created

during the build process. In the next step, it calls the second procedure, called

DEMO_INTRO.PredictiveAnalysis. Finally, it inserts the result set into the data target.

This procedure is normally the one that your applications will call.


CREATE PROCEDURE "DEMO_INTRO"(IN DEMO_INTRO_DATASOURCE_TAB"DEMO_INTRO.DEMO_INTRO_DataSource__TT")


BEGINPREDICTIVEANALYSIS_PARAMETERS_TAB =

SELECT * FROM "DEMO_INTRO.PredictiveAnalysis_PARAMETERS";CALL "DEMO_INTRO.PredictiveAnalysis"(:DEMO_INTRO_DATASOURCE_TAB,

:PREDICTIVEANALYSIS_PARAMETERS_TAB,PREDICTIVEANALYSIS_RESULT_TAB,PREDICTIVEANALYSIS_CENTER_POINTS_TAB );

INSERT INTO "DataTarget" ("ID", "ASSIGNED_CLUSTER", "DISTANCE")SELECT "ID" AS "ID",

"ASSIGNED_CLUSTER" AS "ASSIGNED_CLUSTER","DISTANCE" AS "DISTANCE"FROM :PREDICTIVEANALYSIS_RESULT_TAB;

END

Listing 10.4 SQLScript Procedure

Listing 10.5 shows some of the code of the second procedure. The interesting thing

here is that this isn’t written in SQLScript, but in AFLLANG. The AREA parameter

shows as AFLPAL, showing that it’s calling a PAL function. The FUNCTION is shown as

ACCELERATEDKMEANS. In the PARAMETERS section, you can see the input fields that we

specified in Figure 10.13. The PARAMETERS section is basically in JavaScript Object

Notation (JSON) format.

CREATE PROCEDURE "SHINE_CORE_1"."DEMO_INTRO.PredictiveAnalysis"(IN p1 "SHINE_CORE_1"."DEMO_INTRO.PredictiveAnalysis_DATA__TT",IN p2 "SHINE_CORE_1"."DEMO_INTRO.PredictiveAnalysis_PARAMETERS__TT",OUT p3 "SHINE_CORE_1"."DEMO_INTRO.PredictiveAnalysis_RESULT__TT",OUT p4"SHINE_CORE_1"."DEMO_INTRO.PredictiveAnalysis_CENTER_POINTS__TT")

LANGUAGE AFLLANGSQL SECURITY INVOKERREADS SQL DATA AS

BEGINAFLLANGVERSION='2';SCHEMA='_SYS_AFL';AREA='AFLPAL';FUNCTION='ACCELERATEDKMEANS';PARAMETERS=';

"TABLE",in,TrexTable,,,,;"ID",,,INT,,INTEGER,;"QUANTITY",,,DOUBLE,,DOUBLE,;"ITEM_NAME",,,STRING,,NVARCHAR,50;…


';END

Listing 10.5 AFLLANG Procedure

We can use these two procedures to make our lives easier when we want to use our

flowgraph model in a calculation view.

Using Predictive Models in Calculation Views

We can’t call a SQLScript procedure in a calculation view because calculation views

are read-only artifacts and may therefore only use other read-only artifacts. Proce-

dures can be read-write artifacts. Calculation views may, however, use table func-

tions, as these are read-only. There are three different ways of calling the PAL

algorithm from a table function:

� Copy the code from the created SQLScript procedure into a new table function,

and modify it slightly. We’ll look at this soon.

� Create an AFLLANG procedure, and call this procedure from your table function.

You can create an AFLLANG procedure by creating an .hdbafllangprocedure file.

You’ve already seen the JSON-formatted section where you define the name of

the PAL function and its input and output parameters. If you want to create

such a file, you first need to learn AFLLANG, and then create several tables, table

types, CDS entities, or data structures. Each parameter of the PAL function

needs to be mapped to an existing object.

� Call the documented PAL SQLScript procedures. This requires the assignment of

additional authorizations.

Modifying the SQLScript procedure from Listing 10.4 for the function is actually

quite easy. You can copy the code of the entire procedure. You then follow a few

steps:

1. Change the PROCEDURE to a FUNCTION. To avoid confusion, we changed the name

by adding a number 1. You can also add _FUNC to the name.

Most of the code stays the same, except that a function returns the results set,

whereas the procedure inserted it into the data target.

2. Add a RETURNS table with the definition of the output table type in the FUNCTION

header.

3. Delete the INSERTS INTO “DataTarget” command to RETURNS.

The result is shown in Listing 10.6.


CREATE FUNCTION "DEMO_INTRO1"(IN DEMO_INTRO_DATASOURCE_TAB"DEMO_INTRO.DEMO_INTRO_DataSource__TT")

RETURNS table (ID NVARCHAR(50), ASSIGNED_CLUSTER INTEGER,DISTANCE DOUBLE)


BEGINPREDICTIVEANALYSIS_PARAMETERS_TAB =

SELECT * FROM "DEMO_INTRO.PredictiveAnalysis_PARAMETERS";CALL "DEMO_INTRO.PredictiveAnalysis"(:DEMO_INTRO_DATASOURCE_TAB,:PREDICTIVEANALYSIS_PARAMETERS_TAB,PREDICTIVEANALYSIS_RESULT_TAB,PREDICTIVEANALYSIS_CENTER_POINTS_TAB);

RETURN SELECT "ID" AS "ID","ASSIGNED_CLUSTER" AS "ASSIGNED_CLUSTER","DISTANCE" AS "DISTANCE"FROM :PREDICTIVEANALYSIS_RESULT_TAB;

END;

Listing 10.6 Modifying the Code from the SQLScript Procedure to Create a Table Function

You can now add your table function into a Projection node in the calculation view

in Figure 10.17. Add a new Projection node, and click Add Data Source. When you

search for “DEMO_INTRO”, you’ll see your table function. Click on Finish to add it

as the data source for your calculation view.

You’re now using your predictive model that you created using a flowgraph

directly in your calculation view.

Figure 10.17 Using the Table Function in a Calculation View


Creating a Predictive Model Using SQLScript

You can also create a predictive model purely by using SQLScript instead of graph-

ical modeling tools. This is the way that predictive models were originally created

in SAP HANA before the graphical tools existed.

Since SAP HANA 2.0 SPS 02, SAP provides SQLScript procedures that you can use to

directly use the PAL algorithms. These procedures are found in the _SYS_AFL

schema. This makes the SQLScript code a lot easier and shorter. Before SPS 02, you

had to write your own wrapper code, which could be quite time-consuming. This is

now easy enough that you could even use these procedures interactively in the

SQL Console.

You’ve now mastered predictive modeling and machine learning using the PAL in

SAP HANA.


In this chapter, we looked at the following important terms:

� Full-text index

Depending on what you specify when you create the full-text index, it gives

access to one or more text features. The full-text index will use additional mem-

ory to provide the requested text features.

� Fuzzy text search

This search type enables or disables the fault-tolerant search feature.

� Text analysis

Text analysis provides a vast number of possible entity types and analysis rules

for many industries in many languages.

� Text mining

When using text mining in SAP HANA, you can perform the following tasks:

– Categorize documents.

– Find top-ranked terms related to a particular term.

– Analyze a document to find key phrases that describe the document.

– See a list of documents related to the one you asked about.

– Access documents that best match your search terms.

� Predictive Analysis Library (PAL)

Defines functions that you can use to perform various predictive analysis algo-

rithms.


Practice Questions









1. Which text feature do you use to find sentiment in textual data?

� A. Full-text index

� B. Fuzzy text search

� C. Text analysis

� D. Text mining

2. You create a full-text index. For what text feature clause do you need to spec-

ify a configuration?

� A. SEARCH ONLY

� B. FUZZY SEARCH INDEX

� C. TEXT MINING

� D. TEXT ANALYSIS

3. What is the recommended way to create full-text indexes?

� A. User-defined functions

� B. Procedures

� C. Core data services

� D. SQL statements in the SQL Console

4. For which data types does SAP HANA automatically create full-text indexes?


� A. NVARCHAR

� B. SHORTTEXT


� C. BINTEXT

� D. CLOB

� E. TEXT

5. What predicate do you use for fuzzy text search in the WHERE clause of a SQL

statement?

� A. LIKE

� B. ANY

� C. IN

� D. CONTAINS

6. What do you use in a SQL query to sort fuzzy text search results by relevance?

� A. RANK()

� B. SCORE()

� C. SORT BY

� D. TOP

7. True or False: The full-text index is stored in table $TA.

� A. True

� B. False

8. Which two elements are part of a text analysis configuration? (There are 2 cor-

rect answers.)

� A. Entity extraction

� B. Dictionary

� C. Noun groups

� D. Rule set

9. What PAL algorithm category can you use for supervised learning?

� A. Classification

� B. Clustering

� C. Association

� D. Social network analysis


10. What PAL algorithm would you use to analyze shopping baskets?

� A. K-means

� B. Decision tree

� C. Apriori

� D. Link prediction

11. What must be installed for PAL to work?

� A. SAP HANA BFL

� B. SAP HANA AFL

� C. SAP HANA APL

12. True or False: The scriptserver must be added to the SYSTEMDB database for

PAL to work properly.

� A. True

� B. False

13. Which files can you use to call PAL functions? (There are 3 correct answers.)

� A. .hdbcds

� B. .hdbprocedure

� C. .hdbworkspace

� D. .afllangprocedure

� E. .hdbflowgraph

14. Which runtime behavior type for a flowgraph can process data in real time?

� A. Transactional task

� B. Procedure

� C. Batch task

15. Which data sources can you use in the Data Source node of a procedure-type

flowgraph? (There are 2 correct answers.)

� A. A calculation view

� B. A table type


� C. A CDS view

� D. A template table



You use text analysis with the EXTRACTION_CORE_VOICEOFCUSTOMER configuration

to find sentiment in textual data.


You need to specify a configuration with the TEXT ANALYSIS clause when you cre-

ate a full-text index.


The recommended way going forward to create full-text indexes is using CDS.

We never recommend using SQL statements in the SQL Console because you’ll

have to retype everything again in the production system. Procedures are

acceptable and usable, but CDS is the recommended way.


SAP HANA automatically creates full-text indexes for TEXT, BINTEXT, and SHORT-

TEXT. For NVARCHAR and CLOB, you have to create full-text indexes manually.


You use the CONTAINS predicate for fuzzy text search in the WHERE clause of a SQL

statement. The LIKE predicate only uses exact matching, not fuzzy text search.


You use the SCORE() function in a SQL query to sort fuzzy text search results by

relevance. RANK, SORT BY, and TOP can’t be used to sort SAP HANA fuzzy text

search results.


False. The full-text index is NOT stored in table $TA.


A text analysis configuration consists of a text analysis configuration file, a dic-

tionary, and a rule set.

Entity extraction and noun groups are some of the results of a text analysis.


You can use the algorithms in the PAL classification category for supervised

learning.

Takeaway Chapter 10 457

Clustering is sometimes also called unsupervised learning.


Use the Apriori PAL algorithm to analyze shopping baskets.


You must install AFL for PAL to work.

BFL is also part of AFL, but a completely separate library from PAL.

APL is the automated predictive library and is a separate product.


False. The scriptserver must actually be added to the tenant database for PAL to

work properly. It can’t be added to the SYSTEMDB database.


PAL functions can be called from flowgraphs (.hdbflowgraph), AFLLANG proce-

dures (.afllangprocedure), and SQLScript procedures (.hdbprocedure).

.hdbcds and .hdbworkspace files can’t call code.


A runtime behavior of the transactional task type for a flowgraph can process

data in real time.

Procedure and batch task types of runtime behavior for a flowgraph can’t pro-

cess data in real time.


You can use table types and CDS views as data sources in the Data Source node

of a procedure-type flowgraph.

You can’t use a calculation view as a data source in the Data Source node of a

procedure-type flowgraph.

A template table can only be used in a Target Data node; not in a Data Source

node.

Note

Some questions in the certification exam will only have three answer options.

Takeaway

You now know how to create a text search index for use with the fuzzy text search,

text analysis, and text mining features in SAP HANA. You can use these features to


provide fault-tolerant searches, sentiment analysis, and document references for

the users of your information models.

We also discussed the categories and some of the algorithms in PAL and learned

how to create advanced predictive analysis models for machine learning.

Summary

We discussed a few aspects of text analysis: how to search and find text even if the

user misspells it, how to analyze text to find sentiment, and how to mine text in

documents. We explained how to build a predictive analysis model for business

users using the flowgraph editor, and we saw how to get the system to automati-

cally create procedure code to be used in calculation views.

In the next chapter, we’ll look at information modeling with spatial, graph, and

series data.

Chapter 11

Spatial, Graph, and Series Data Modeling


� Understand the key components of spatial processing, such as

spatial reference systems (SRSs), data types, joins, predicates,

functions, expressions, and clustering

� Learn how to use the key spatial components in both graphical

and SQLScript modeling

� Work with relationships between data objects using graphs

� Get an overview of SAP HANA series data

� Understand the advantages of using SAP HANA for series data

� Get to know some of the SAP HANA series data functionalities

Chapter 11 Spatial, Graph, and Series Data Modeling460

After understanding the basics of SAP HANA information modeling, we started by

taking your knowledge to the next level in the previous chapter with text process-

ing and predictive modeling. In this chapter, we’ll cover three more topics: spatial,

graph, and series data modeling. All five topics can be used independently from

each other, but they can also be combined in projects. We’ll look at an example of

how to combine spatial and graph modeling in this chapter.

We’ll begin our discussion with spatial processing and how to combine business

data with maps and geospatial capabilities to extract more value. We already met

the Graph node in calculation views. Here we’ll take another quick look at the

topic. After that, series data is the fifth and last topic that will complement your

knowledge of advanced SAP HANA modeling techniques. In this chapter, we’ll just

provide an overview on the topic. You can combine series data with aggregation,

analysis, and predictive modeling to create valuable insights into real-world oper-

ations.

Real-World Scenario

You’re working on an SAP HANA project at a pharmaceutical company. The

company has created a mobile web application with SAP HANA for its sales

representatives.

To provide the sales reps with a list of new potential customers and to save

them time by not contacting people working in nonrelated fields, you ana-

lyze the relationships between people in your SAP Customer Relationship

Management (SAP CRM) system.

You add some what-if map-based analytics to the mobile app so that sales

reps can change the areas where they are working and see the impact on their

projected sales figures.

The company also has a fleet of vehicles for deliveries of their products. All

the vehicles are fitted with tracking devices, as well as a variety of sensors to

monitor the vehicles. The company wants a dashboard that shows where the

vehicles are (time series data), what they are doing (e.g., loading goods or

driving), and when they will be at their destinations. This dashboard shows a

combination of real-time data, inferred status information, and arrival time

predictions.


You also notice that by analyzing the historical data, you can add planned

dates for servicing the vehicles based on their mileage, the way the drivers

handled their vehicles, and warnings from sensors in the vehicles You can

then calculate projected running costs of the fleet and even show if the com-

pany needs fewer or more vehicles in its fleet.



knowledge of advanced modeling with SAP HANA.


topics:

� The key components of spatial processing, such as SRSs, data types, joins, pred-

icates, functions, expressions, and clustering

� The use of spatial components in both graphical and SQLScript modeling

� The key concepts of graph processing

� Basic series data modeling techniques

� How to create a series table

Note



In this chapter, we’ll look at spatial and geospatial processing, graph modeling, and

series data. From the real-world scenario above, you can get some idea of what is

possible with the knowledge you’ll gain from this chapter. The real value starts

appearing when you learn to combine several of these modeling capabilities.

One example that you’ll most likely have used is GPS routing. The destination is a

geospatial point. The roads linking your current location to your destination form

a graph. Each road has certain characteristics, for example, the speed limit and

whether it’s a tarred or tolled road. In this case, you combine spatial with graph

modeling.


Another example is when you order items from an online store. Business informa-

tion from your order, such as the items and whether you paid for same-day deliv-

ery, is linked to the geospatial data of your address. The vehicle is most likely

monitored for real-time feedback, the data is stored using series data, and this his-

torical data is processed with predictive algorithms to determine the next mainte-

nance service. The online store can use machine learning with weather and traffic

patterns to determine when best to deliver your order, and it can use text process-

ing from social media to determine if people are satisfied with their purchases.

The first topic we’ll discuss in this chapter is spatial processing in SAP HANA.

Spatial Modeling

SAP HANA has very strong native spatial data processing capabilities, and they just

keep getting better with each new version.

Companies want to be able to use all the data they have in useful ways. They might

have a variety of tools, their data is often captured in different formats, and they

use different services. Compatibility and integration is therefore a must-have

requirement when working with geospatial data. You might get data from satel-

lites or weather services. How do you combine this data with the data in your own

databases, even when some departments in the company are using different

open-source and commercial tools?

SAP HANA is certified Open Geospatial Consortium (OGC) compliant. The OGC is a

consensus standards organization that creates open standards for geospatial con-

tent and services. As an industry standard, it’s important that SAP HANA conforms

to these standards. One of these standards is ISO/IEC 13249-3 SQL/MM Spatial (the

“MM” is for multimedia). These specify SQL functions that you can use to manipu-

late spatial data in SQL code. We’ll look at these functions in this section.

The Environmental Systems Research Institute (ESRI) is the largest international

software supplier of geographic information system (GIS) software, and it has

about 43% of the worldwide GIS software market share. ESRI has an entire suite of

GIS software, called ArcGIS. ESRI developed the ESRI shapefile vector format, which

has become a de facto standard to transport spatial data. SAP HANA is ESRI GIS

Enterprise Geodatabase certified. SAP HANA also works with several map provid-

ers, such as HERE and OpenStreetMap. HERE was formerly known as Navteq, and

then Nokia Here maps. OpenStreetMap is a collaborative project, like Wikipedia,

that creates a free and editable map of the world.


We’ll discuss some of the SAP HANA spatial features and capabilities in this sec-

tion. We’ll start by looking at how to store two- and three-dimensional spatial data

in SAP HANA, and how to use the data in calculation views using spatial joins,

expressions, functions, and predicates. We’ll demonstrate that you can enrich the

master data of your customer data by geotagging and geocoding to provide longi-

tude and latitude information to customer addresses. You can then visualize and

analyze your enterprise data using standard SAP analytics clients, such as SAP

Analytics Cloud. We’ll also take a quick look at spatial clustering and the SAP HANA

spatial services.

Tip

Geospatial processing is a subset of spatial processing related to geography. Spatial pro-

cessing can include CAD models, augmented reality (AR), virtual reality (VR), or even mod-

els of the human body, and it uses a three-dimensional coordinate system that differs

from the geospatial coordinates of longitude, latitude, and height above sea level. In this

chapter, we’ll focus on the geospatial subset.

As with any new topic, half the work is learning the vocabulary. We start by clear-

ing up some misconceptions that you might still have from your school days.

Spatial Reference Systems

Most schools have a big map of the world somewhere on a wall. Most often this

map is the Mercator map of the world, as illustrated on the left side of Figure 11.1.

There are many advantages to using this map. It’s nice rectangular format fits

nicely on a classroom wall. The biggest advantage is that you can plan routes from

one place to another using straight lines. Just think about that for a moment. The

earth is a three-dimensional sphere. To travel long distances, you’ll actually travel

over the horizon, that is, in a three-dimensional curve. This map simplifies it to a

flat straight line using a Cartesian coordinate system, which makes calculation

easy and fast.

The disadvantage is that to accomplish these amazing feats, it has to distort reality.

On the Mercator map shown in Figure 11.1, it seems that Greenland and Africa are

about the same size, yet Africa is 14 times larger than Greenland. You can see the

actual distortion by using the map tool on http://s-prs.co/v487626 or looking at the

map graphic at http://s-prs.co/v487627.




Our GPS maps and devices on the other side use the more accurate three-dimen-

sional model of the earth. Just to clarify; the earth isn’t a perfect sphere with a con-

stant radius of 6371 km. In reality, it’s slightly flatter at the poles, or bulging at the

equator, depending on your viewpoint. Look at the right side of Figure 11.1. The

radius to the poles (shown as p) is about 6 357 km, while the radius to the equator

(shown as e) is about 6 378 km. The earth is a spheroid. And even this is an extreme

simplification, as the earth has “bumps” in places. Working with calculations in

this model is most complex and therefore slower. This model uses the spatial ref-

erence system (SRS) called WGS84, where WGS stands for World Geodetic System.

Each SRS has its own specifications; for example, WGS84 uses distances in meters,

whereas another SRS could use feet. Traditionally, the latitude is given before the

longitude, but in a Cartesian coordinates system, you usually mention the x-coor-

dinate before the y-coordinate; therefore, some round-earth SRSs use longitude

before latitude.

Most commonly, people refer to the system shown in Figure 11.1 as “flat-earth” and

“round-earth” models. Each of these models has its advantages and disadvantages.

Figure 11.1 Two Popular Spatial Reference Systems

Each of the SRSs has a spatial reference system identifier (SRID). The SRID for WGS84

used by GPS is 4326. There are hundreds of SRIDs. For example, SRID 3857 is a

spherical Mercator projection coordinate system used by web services such as

Google Maps and OpenStreetMap. And there is WGS84 (planar), with SRID

1000004326, that is similar to WGS84 except that it’s a flat-earth type of SRS that

uses an equirectangular projection of the accompanying distortion to length and

area.

(Web) Mercator ProjectionFlat-earth SRS with

Cartesian Coordinate System

p

e

WGS84 (World Geodetic System)

Geographic Coordinate SystemRound-earth SRS with

ST_Transform ([SRID])


Tip

As a best practice, always specify the SRID when working with spatial data. Your assump-

tions don’t always work, and it’s easy to make mistakes when the SRID isn’t specified.

If your data is stored in the 4326 format, but you want to use it with data available

in these systems, you’ll need to transform your data to the 3857 format (or trans-

form the other data to 4326). You can do this using the ST_Transform function. We’ll

discuss spatial functions later in this section.

Sometimes you also need to transform your data to a different SRS if the spatial

functions you want to use don’t work in the SRS of your data. For example, some

of the spatial functions in SAP HANA don’t support round-earth SRSs. You’ll have

to transform your data to a planar SRS before calling those functions.

SPS 04

The spatial functions in SAP HANA that don’t support round-earth SRSs are decreasing

with every new release of SAP HANA.

SAP HANA also has a default SRS of 0, which is a two-dimensional (planar) system

that is strictly Cartesian without any relationship to the earth’s shape. This SRS can

be used, for instance, to reference a space of limited dimensions, such as an office

building, a factory, or a cornfield.

SPS 04

The in-memory representation of geometries in planar SRSs has been optimized and now

uses up to 50% less memory. This memory footprint reduction is important to lower the

total cost of ownership (TCO) for SAP HANA.

You need to specify the SRID when creating tables with spatial data in SAP HANA.

Creating Tables with Spatial Data

When you work with graphics programs on your computer, it often shows you the

position of your mouse or pen cursor with coordinates (x, y). Spatial data is often

also stored this way. Or your GPS shows you a point of interest (POI) such as a

restaurant as latitude and longitude, depending on what SRS it uses. This is similar

to (x, y).


Sometimes you also need the height above or below sea level, for example, when

you’re flying a glider, climbing a mountain, or working in a mine. In this case, you

store this value in the z-coordinate, and your POI is shown as (x, y, z).

Furthermore, you also might need to store a measure with the three-dimensional

POI. This measure could be the air pressure for weather prediction, wind speed

while flying a glider, or temperature underground in the mine. You can store this

in the m-coordinate, and your POI is then shown as (x, y, z, m).

Figure 11.2 shows a list of the spatial data types in SAP HANA with graphical exam-

ples.

The point position is the most basic spatial data type. The OGC standard states that

a point value can include a z- and/or m-coordinate value. In SAP HANA, all spatial

data and code is prefixed with ST_. The point spatial data type in SAP HANA is

ST_Point.

Tip

The ST_POINT spatial data type has the limitation that it only stores two-dimensional

geometries. No z- or m-coordinate can be stored in a ST_POINT spatial data type. If you

need z- or m-coordinates, you should use the ST_GEOMETRY spatial data type.

Each of the spatial data types illustrated in Figure 11.2 is available in SAP HANA and

is prefixed by with ST_.

Figure 11.2 Spatial Data Types

MultiLineString CircularStringLineStringMultiPointPoint

GeometryCollectionMultiPolygonPolygon Polygon (ring)


Figure 11.3 shows the hierarchy of the spatial data types. The top data type is ST_

Geometry, and it’s a superset of all the other spatial data types.

The following list describes the spatial data types:

� ST_Geometry

This is the supertype for all the other spatial data types; you can store objects

such as linestrings, polygons, and circles in this data type. Then you can use the

TREAT expression to specify which data type you want to work with. For exam-

ple, TREAT( geometry1 AS ST_Polygon ).ST_Area() will take a geometry, treat it like

a polygon, and ask the size of the polygon’s area.

� ST_Point

A point is a single location in space with x- and y-coordinates at a minimum.

Points are often used to represent addresses, as in the shop example in Figure

11.4 in the next section.

� ST_LineString

A linestring is a line, which can be used to represent rivers, railroads, and roads.

In the shop example, it could represent a delivery route.

� ST_CircularString

A circularstring is a connected sequence of circular arc segments, much like a

linestring with circular arcs between points.

� ST_Polygon

A polygon defines an area. In the shop example, this is used to represent the

suburbs in the second table (see Figure 11.4 in the next section; most suburbs

don’t have regular square shapes).

� ST_MultiPoint

A multipoint is a collection of points. For example, it could represent all the dif-

ferent locations (addresses) where a company has shops.

� ST_MultiLineString

A multilinestring is a collection of linestrings. In the shop example, this collec-

tion could include all the deliveries that a shop has to make on a certain day for

which the driver goes to different customers or suppliers.

� ST_MultiPolygon

A multipolygon is a collection of polygons; for example, the suburbs could be

combined to indicate the area of the city or town.

� ST_GeometryCollection

A geometry collection is a collection of any of the spatial data types mentioned

in this list.


Figure 11.3 Hierarchy of the Spatial Data Types

Now that you know the spatial data types, it’s easy to created tables using core data

services (CDS). In Listing 11.1 you can see the CDS code used for creating two exam-

ple tables. The first table stores the location of the shops or branches of a company.

In this case, ST_Point is OK because we only need the longitude and latitude of the

shops. The second table stores the shapes of all the suburbs in the city.

namespace spatial.spatialdb;

context spatialtables{

entity GEO_SHOP {key SHOP_ID: Integer not null;SHOP_NAME: hana.VARCHAR(100);SHOP_LOCATION: hana.ST_POINT;

};

entity GEO_SUBURB {key SUBURB_ID: Integer not null;SUBURB_NAME: hana.VARCHAR(100);SUBURB_SHAPE: hana.ST_GEOMETRY;

};};

Listing 11.1 CDS Syntax for Creating Two Tables with Spatial Data

We can now use these tables in our calculation views.

SPS 04

As from SAP HANA 2.0 SP 04, we can now also store spatial data in warm and cold storage

using native storage extension (NSE). We discussed data aging in Chapter 3. This helps to

lower the TCO for SAP HANA for customers with lots of spatial data.

ST_Geometry

ST_Point ST_LineString ST_CircularString ST_Polygon ST_GeometryCollection

ST_MultiPoint ST_MultiLineString TS_MultiPolygon


We first mentioned spatial processing in Chapter 5 when we discussed spatial

joins. Now, let’s take a closer look at spatial joins in an SAP HANA system.

Spatial Joins and Predicates

Figure 11.4 shows two tables that contain spatial data. The left table, called GEO_SHOP,

contains shop locations, and the right table contains city suburb shapes. We use

these two tables in a calculation view. In this case, we put them in a Join node.

Figure 11.4 Two Tables Containing Spatial Data with a Spatial Join

You can join different data spatial types together in a spatial join. Figure 11.5 shows

the details of a spatial join. Notice that the SPATIAL JOIN section is available for this

join type. The Predicate dropdown list in this section allows you to specify how to

join the fields.


In the PROPERTIES section, we see the join showing up as an inner join, but this is

grayed out because our join is between fields containing spatial data. When you

click on the create join button on the top-right toolbar of Figure 11.4, you get the

screen shown in Figure 11.5.

In the shop example, we’ll use the Contains option because we want to see which

suburb (right table) contains which shop addresses (locations). In other words, we

want to know in which suburb each shop is located.

Tip

Don’t confuse the Contains option in the spatial joins with the CONTAINS predicate we

used when executing a fuzzy text search in the previous chapter.

Figure 11.5 Spatial Join Details

So, when do you use which predicate? Figure 11.6 illustrates many of the spatial

predicates. Most of these are quite obvious; for example, the first predicate shows

that the Point is WITHIN the triangle.


Figure 11.6 Spatial Predicates

There are a few things to watch out for though:

� We think of shapes as, for example, a triangle or an ellipse with a surface area.

There is the area of the shape and the exterior, which is everything outside the

shape. But there are actually three “areas” to watch out for. There is the inside

area, the exterior outside, and the boundary. Think of the boundary as the skin

around your body, separating your insides from the outside. A shape can have

more than one boundary; for example, a doughnut or ring shape has a round

boundary on the outside, as well as a round boundary on the inside for the hole.

Look at Figure 11.9 in the middle left to see an example of such a shape.

� The ST_Touches predicate is where another shape overlaps with one or more

points of the boundary but doesn’t overlap with any points inside. Hence,

returning to the analogy of thinking of the boundary as your skin, this is what

happens when someone touches you.

.ST_Within ( )

.ST_Disjoint ( )

.ST_Equals ( )

.ST_Crosses ( )

.ST_Touches ( )

.ST_Contains ( )

.ST_Intersects ( )

.ST_Overlaps ( )

.ST_Intersects ( )

.ST_Covers ( )

.ST_Contains ( ) = false


� ST_Crosses and ST_Intersects is when a line or polygon goes over the boundary

into the shape. Typically, this is used in geospatial applications to see where

roads or railway lines cross rivers or enter another country.

� There is a subtle difference between ST_Contains and ST_Covers in that ST_Con-

tains means a smaller shape is fully enclosed inside the area of the main shape.

But what happens when a point is on the boundary of the main shape? In that

case, the ST_Contains predicate returns false as there is a difference between the

boundary and the area. ST_Covers on the other hand means there are no points

of the smaller shape on the exterior of the main shape. When a point is on the

boundary of the main shape, the ST_Covers predicate returns true as the point

isn’t outside the main shape. (ST_Covers isn’t part of the OGC standard, but it’s

available in SAP HANA.) ST_Covers extends the ST_Contains predicate to be more

“tolerant" for the boundaries. ST_Covers and the symmetrical ST_CoveredBy

methods support round-earth SRSs, which isn’t the case for ST_Contains and

ST_Within, which work only on flat SRSs.

For a different business case, you could use the GEO_SHOP table on both sides of the

join in Figure 11.5 and use a Within Distance predicate in the join. You can then find

all the shops within a certain distance of a selected shop. Banks typically use some-

thing similar to show you the nearest ATM or bank branches from your current

location.

Figure 11.7 shows an SAP demo application that looks at gas lines in a city (dark

lines along some of the streets) and shows when they get within a certain distance

of buildings where they could become a problem. With 10 million pipeline assets

across the country, SAP HANA accelerated the application from 3.5 hours to 2.5 sec-

onds. With response times of 2.5 seconds, such an application becomes real time.

This screen is zoomed into a specific area, which is why it shows “only” 642 pipes

and 1,455 buildings. The response time was less than half a second. This is then

combined with business processes by linking the results back to asset manage-

ment and work management systems.

Spatial Expressions

Apart from spatial joins, we can also use spatial data in calculated columns and fil-

ter expressions. Figure 11.8 shows how you can create a spatial expression in a cal-

culated column. In the Functions tab, you select the Spatial Predicates you want to

work with. In this case, we used the Contains predicate that we used in the spatial

join.


Figure 11.7 SAP Demo Application on OpenStreetMap Using a Spatial Distance Join

Figure 11.8 Spatial Expressions in Calculation Views


More than 80 Spatial Functions are available.

SPS 04

More than 100 spatial functions are available in SAP HANA 2.0 SPS 04.

Figure 11.9 shows some the spatial functions and their results. We start with a tri-

angle and ellipse on the top left side, and apply four different functions to these

shapes on the top line.

Figure 11.9 Spatial Functions and their Results

In the second line, we see an example of the boundary and area terms we’ve

already discussed. The second and third items on the second row show two func-

tions that are used quite often. When we have a bunch of points, we often want to

find a shape that encloses all these points. The easiest is by creating a rectangle,

called an Envelope. This is easy because we just have to look for the minimum and

maximum values of all the x and y values in the points. The other one is where we

want a smaller polygon to enclose all the points. For this we create a Convex Hull.

When you meet a function with Aggr (for aggregation) in the name, it can be

applied to a collection of shapes. The ST_UnionAggr of all “dog shapes” will result in

a “dog.”

EnvelopeST_EnvelopeAggr()

Convex HullST_ConvexHullAggr()

Boundary and Area

ST_Union() ST_Intersection() ST_Difference() ST_SymDifference()Original Shapes

ST_IntersectionAggr() ST_UnionAggr()of “dog parts”

Result is “dog”Result is emptyResult is polygonthat contains all points

Result is rectanglethat contains all points


Geocoding in an SAP HANA Flowgraph

Geocoding is the process of converting address information into geographic loca-

tions, such as latitude and longitude. Because business data is associated with

address information without location information, geocoding is important to

enable geographic analysis of such address data. In many businesses, huge value is

unlocked by using this additional type of analysis.

Reverse geocoding does exactly the reverse, as the name implies. It takes geospa-

tial data such as latitude and longitude, tells you what company is located there,

and returns the address data.

This functionality needs a geocoding index. This is very similar to the full-text

index we already discussed in the previous chapter, except that we work with

address and geospatial data instead of text data.

SAP HANA smart data quality, that we discussed in Chapter 9, provides native

geocoding and reverse geocoding capabilities in SAP Web IDE for SAP HANA. This

can be accessed with a Geocode node in a graphical flowgraph model. In Figure

11.10, we combined the Geocode node in an SAP HANA smart data quality workflow

that also includes data cleansing.

Figure 11.10 Flowgraph Model with a Geocode Node


Spatial Clustering

Let’s say you need to determine where to build a new clinic or hospital in an area.

You can look at a map to see where other clinics and hospitals are and make sure

you’re far enough away from the existing ones. But you’ll most likely end up in an

area where there are very few people, and building the clinic or hospital will just be

a waste of money. You also need to look at the population densities, and the gen-

eral demographics of the areas. The same goes if you want to open a new branch

for your business.

You can use spatial clustering to group sets of points together in clusters that meet

your criteria. This is similar to the Predictive Analysis Library (PAL) algorithms,

such as k-means, that we discussed in the previous chapter, except that you now

work with data that also has geospatial properties.

In SAP HANA SPS 03, you can only use spatial clustering for two-dimensional

points in a flat-earth SRS.

Figure 11.11 shows the spatial clustering algorithms available in SAP HANA, as

described here:

� The first clustering algorithm (top left) is very easy. You merely throw a grid

across the points and count how many points are inside each cell. In the last col-

umn, we show the counts for each cell as an example. Figure 11.12 shows what

this looks like on a real map. Here we have a database of POIs in Europe. The

advantage of the grid clustering algorithm is that it’s easy and fast, quickly pro-

viding us an idea of the distribution of the points.

SPS 04

The second clustering algorithm (top right) is very similar to the grid clustering algorithm.

It just uses hexagons instead of rectangles. In certain cases, it can give more visually

appealing results.

� The k-means clustering algorithm works well when the points tend to be in

spherical clusters. It’s centroid-based and gives better insights than simple grid

or hexagonal clustering. However, this algorithm is more complex and takes

longer to process. Figure 11.13 shows the same map and data set as was used in

Figure 11.12 but using the k-means clustering algorithm.


� The DBSCAN clustering algorithm works best with nonspherical clusters. It’s

density-based, meaning it requires a certain number of data points in the neigh-

borhood of a point. You can set the radius of the neighborhood.

Figure 11.11 Spatial Clustering Algorithms

Figure 11.12 shows the grid clustering algorithm used on a map of Europe with a

database of POIs, such as restaurants, museums, hospitals, schools, ATMs, and

public transport. You can choose which type of POI you want, zoom into the area

you plan to visit, and choose the spatial clustering algorithm to analyze the data

for that area and POI category.

Figure 11.13 shows the same map but using the k-means clustering algorithm.

Hexagonal Grid (from SPS 04)

DBSCAN

Rectangular Grid

5

2

3

0

2

4

1

0

K-means


Figure 11.12 SAP Demo Application Using the Grid Spatial Clustering Algorithm

Figure 11.13 Results in an SAP Demo Application with the K-means Spatial Clustering Algorithm

Spatial Functions Using SQLScript

You saw an example of a spatial function used with SQLScript earlier in the “Spatial

Joins and Predicates” section, where we asked for the area of a polygon to be calcu-

lated, for example, geometry1.ST_Area().

You can ask if a shop’s location is in a suburb as follows:


SUBURB_SHAPE.ST_CONTAINS(SHOP_LOCATION1)

You can also see how far two shops are from each other as follows:

SHOP_LOCATION1.ST_WITHINDISTANCE(SHOP_LOCATION2)

You can use a spatial join with the ST_Within predicate. When ST_Within returns 1,

it means shape b is inside shape a, as follows:

SELECT b.id as b_within, a.name, a.shape, b.point FROM A, B WHERE b.point.ST_Within( a.shape ) = 1;

If you want to find all the shapes that aren’t within shape a, you just use a spatial

join where ST_Within returns 0, as follows:

SELECT b.id as b_not_within, a.name, a.shape, b.point FROM A, B WHERE b.point.ST_Within( a.shape ) = 0;

You can see that the spatial functions are used like methods in object orientation,

using a dot-notation.

Note

You can find all the spatial functions and their descriptions in the SAP HANA Spatial Ref-

erence guide at http://s-prs.co/v487620.

A software development kit (SDK) is also available that you can use to create your

own custom geospatial algorithms.

Importing and Exporting Spatial Data

In SAP HANA, you can import and export spatial data in the Well-Known Text

(WKT) and the Well-Known Binary (WKB) formats. These formats are part of the

OGC standards. You can also import and export the extended versions of these for-

mats, which are used by PostGIS: Extended Well-Known Text (EWKT) and

Extended Well-Known Binary (EWKB).

In addition, SAP HANA can import all ESRI shapefiles, except the MultiPatch shape

type. SAP HANA can export spatial data in the GeoJSON and Scalable Vector

Graphic (SVG) file formats. The SVG file format is maintained by World Wide Web

Consortium (W3C) and is supported by all modern browsers.



Sometimes you get public spatial data in comma-separated values (CSV) files. As

we discussed before, you can import these using CDS .hdbtabledata files or using

SAP Web IDE for SAP HANA in SAP HANA 2.0 SPS 04.

SAP HANA Spatial Services

In 2018, SAP released a set of spatial services on the cloud. These services include

weather information, earth observation, and satellite imagery, for example, from

the European Space Agency (ESA). You can subscribe to these SAP HANA spatial

services and call them using application programming interfaces (APIs). You can

find more information at http://s-prs.co/v487603.

You can use this set of services, for example, in agricultural scenarios to look at sat-

ellite imagery of biomass. This gives you an excellent overview of how fast crops

are growing or where there are problem areas caused by insects or plant diseases.

Crop insurers can use this information to assess their risk, farmers can use it to cal-

culate crop forecasts, and governments can use it to plan food security.

SPS 04

SAP HANA spatial services is now available inside GeoTools. GeoTools is a free open-

source GIS toolkit that is used by many open-source and commercial software projects.

GeoServer is one of the prominent server-side geoprocessing and data access software

tools that uses GeoTools. These tools can now all access and serve geodata stored in SAP

HANA.

Note

To learn more about spatial processing in SAP HANA, look at the examples in the SAP

HANA Interactive Education (SHINE) demo content. Here, you can find many examples of

spatial processes; for example, screens in which you can select shops on a map on the left

of the screen and see their sales details in the area on the right.

You can find more information about spatial data in the SAP HANA Spatial Reference

Guide. There is also an openSAP course called “Spatial Analysis with SAP HANA Platform”

at http://s-prs.co/v487628 from mid-2017. There is also an SAP HANA Academy playlist for

spatial analysis on YouTube at http://s-prs.co/v487604.

Graph Modeling

Next, we’ll discuss graph processing. In this case, we’re not talking about reporting

graphs, which can be thought of as charts. Instead, think about social media





networks, such as LinkedIn and Facebook, where people are linked to many other

people. If we draw the relationships between hundreds of people, it would result in

a complex spider web-type of diagram. This is referred to as a graph. Graphs are

therefore used to model the relationships and attributes of highly networked enti-

ties.

You can use graph processing together with all the other types of SAP HANA pro-

cessing, such as textual, spatial, and predictive. This way, you can easily build very

complex models that would be very difficult with most other development plat-

forms.

Figure 11.14 shows an SAP demo application for planning music festivals. The demo

was created to show what’s possible with SAP HANA. In this example, a large num-

ber of music fans are spread out over an area.

Figure 11.14 SAP Demo Application Using Spatial and Graph Processing to Plan Music Festivals

The fan base is shown in a heat map in the middle-top analysis of the area. If you

only plan one music festival, not all the fans can come to it due to travel distance,

time, and costs. You can see the roads on the map on the right side. If you have a

hundred music festivals, it will take too much of your time, cost you too much, and

audiences wont’ be large. So, what is the optimal number of music festivals that

you should organize, and where should you have these festivals? The main screen

on the left shows that SAP HANA does the calculations in a few seconds and


suggests 10 festivals at the places shown. It also generates a heat map (middle-bot-

tom map) of the traffic patterns that your music festivals will create.

In this demo, we have spatial data with the locations of where the music fans stay

and where we’ll have the festivals. We also have graph data in the form of the

roads, linking various spatial locations together. We also have to combine this

with business data, such as how far are fans prepared to travel, how much should

the tickets be, what does a music festival cost the organizers, and how much time

is involved with organizing and running such events.

To understand the process of how to create graph models, we’ll start with a small

example of a group of people. From a sketch, we’ll see how to create this in SAP

HANA. After we’ve created the graph, we can view and analyze it using various

graph algorithms. With this background, it will be easy to implement it into a cal-

culation view. We’ll close off the topic by looking at two graph query languages

available in SAP HANA and how graphs are different from hierarchies.

Creating Graphs in SAP HANA

Graph processing allows you to discover patterns in real time on large volumes of

data, which is something that isn’t possible for humans. We can query the graph to

find answers to questions such as the following:

� What are the chances of two specific people going on a date?

� Who is the best person to connect with on LinkedIn to get contact with a buyer?

� In this region, what are the best products to sell to people according to their

likes and dislikes on Facebook?

You could develop such queries using standard SQL, but it would be very complex,

and the performance would probably be really bad. A lot of specialized graph data-

bases have appeared over the past five years. The disadvantage of such specialized

databases is that it becomes difficult to combine it with other data and business

information to solve wider problems and create more powerful solutions.

SAP HANA has, in addition to all its other features, a special graph-processing

engine to handle graph queries elegantly and fast. In SAP HANA 2.0, you can query

the graph data using graphical calculation views, the Cypher Query Language

(Cypher), or GraphScript.

LinkedIn makes suggestions of who you can connect with next based on the peo-

ple you’re currently connected with and who they are connected with. They need

some form of graph processing to do this. It’s quite complex because people work


for companies, some people are looking for new jobs, other people want to show

their thought leadership, some want to advertise their products and services, and

yet others want to recruit someone for a position at another company. People

have different roles and needs, and this shapes the different types of relationships.

For us to understand graph processing, we’ll use a much smaller example. Figure

11.15 shows an example of a few colleagues and their various relationships. The col-

leagues are obviously also working for SAP. There is an organizational structure in

the company with reporting lines. Most of the colleagues are married. Even in this

small group, we already see at least three different types of relationships, and we

have two types of entities: a company and people.

With this small group, we can draw the relationships in a diagram. However, when

we look at hundreds of millions of relationships—such as those on LinkedIn—

drawing such a diagram obviously becomes impractical.

Figure 11.15 Graph Diagram Showing Relationships

Let’s assume we wanted to analyze this graph. We first need to put the data into the

SAP HANA system. For this, you’ll need a table with the people and the company.

These are the nodes in the graph, as illustrated in Figure 11.15. These nodes are

called vertices and are stored in a normal SAP HANA column table. Each vertex can

have multiple attributes, for example, whether this node is a person or a company,

whether the person is male or female, the age of the person, and so on. These attri-

butes are simply additional fields in the column table.

Tshepo

Lerato

Sikhumbuzo Phillip

Hans-Joachim

SAPMichelle

Kele

Person

Company

Works for

Reports to

Married to


In another table, we store the relationships, such as the existing “friends” relation-

ships between these people. These relationships are called edges and are also

stored in column tables. You can have different types of relationships between

people. In Figure 11.15, we have “married to,” which is a bidirectional relationship,

and “works for,” which only has a single direction. The direction of the “works for”

relationship is “outgoing” for the employee of the company and “incoming” for

the company.

The nodes are the people and the company, and the lines are the relationships. The

lines can have flow direction, meaning we can run traces, such as the reporting

lines in a company. From this, we can deduce the organizational structure. In this

case, it would be similar to a hierarchy. But these lines can also flow in “circles,” or

in both directions, which would not be allowed in hierarchies.

The direction of a relationship is determined by the “source” and “target” attri-

butes. For example, the “works for” relationship has the employee’s name in the

“source” field, and the company’s name in the “target” field. While this sounds

very specific, just remember that many tables in a normal business system fit this

pattern. In commercial systems, when you ship a product to someone, you have

pickup and delivery addresses. When you fly, you have departure and arrival air-

ports. Any table with “from” and “to” fields is usable. Even normal SAP HANA

views with financial data according to time periods—with start and end dates—

can be used. Even if a table doesn’t exactly have this format, you can create a view

that does and use the view for the graph processing.

Tip

It’s important to realize that you can use normal tables and information views for graph

processing in SAP HANA with your current business data. You don’t need a separate spe-

cialized database or tables for graph processing when using SAP HANA.

You can use the attributes in vertices and edges when querying the graph. Remem-

ber that these attributes are the additional fields in the database tables or views.

The important piece that you need to enable the graph processing in SAP HANA is

a graph workspace. A graph workspace is a simple file that links the vertex table to

the edge table.

Note

The graph workspace simply contains the definition of the graph model and how the ver-

tices and edge tables relate to each other.


Building the Graph Tables and Workspace

Now that you have an idea of how SAP HANA enables graph processing, let’s create

the graph for Figure 11.15 and then use SAP HANA to analyze it. This simple exam-

ple will make it easy for you to understand the entire process.

We already know that we need two tables: a vertex table for the nodes and an edge

table for the relationships.

In Figure 11.15 we have two types of nodes: people and the company. All the nodes

have a name. Our vertex table therefore just needs two fields: the NAME and the

TYPE. This table is defined in the CDS table definition in Listing 11.2 and is called

MEMBERS. In our case, the names are unique, so we can use the NAME field as the key

field. Otherwise, we would have had to add a key field with unique values for each

record.

The RELATIONSHIPS table needs SOURCE and TARGET fields. Both fields simply use the

NAME (key field) of the person or company from the MEMBERS vertex table. We also

need to specify the TYPE of relationship, for example, “works for.” And we add a KEY

field.

namespace graph.graphteam;

context team{

entity MEMBERS {key NAME: hana.VARCHAR(100) not null;TYPE: hana.VARCHAR(100);

};

entity RELATIONSHIPS {key KEY: Integer not null;SOURCE: hana.VARCHAR(100) not null;TARGET: hana.VARCHAR(100) not null;RELATIONSHIP: hana.VARCHAR(100);

};

};

Listing 11.2 CDS Code to Create Tables for Vertices (Members) and Edges (Relationships)

After you’ve created your .hdbcds file, build it to create the two tables.

The next file we need is an .hdbworkspace file to create the graph workspace. This

is a database artifact file. Listing 11.3 shows how to define the workspace file.


You specify which table or view is your EDGE TABLE and then specify which fields in

it are the SOURCE, TARGET, and KEY fields. Then you specify which table is your VERTEX

TABLE and what field in is its KEY field.

GRAPH WORKSPACE "graph.graphteam::teamgraph"EDGE TABLE "graph.graphteam::team.RELATIONSHIPS"

SOURCE COLUMN "SOURCE"TARGET COLUMN "TARGET"KEY COLUMN "KEY"

VERTEX TABLE "graph.graphteam::team.MEMBERS"KEY COLUMN "NAME"

Listing 11.3 team.hdbworkspace File to Create the Workspace

When you’ve created this simple file, build the file to create your graph workspace.

Open the two tables in SAP HANA database explorer, and add the data from Figure

11.15 in the two tables. Figure 11.16 shows what the result looks like.

Figure 11.16 Inserting the Data from Figure 11.15 into the Vertex and Edge Tables


You’ve now built your first graph in SAP HANA. Next, we’ll view and analyze your

graph.

Viewing and Analyzing Graphs

You used the SAP HANA database explorer to insert data into the vertex and edge

tables. On the top left area, look for the Graph Workspaces, as shown in Figure 11.17.

This shows the list of graph workspaces in your SAP HANA Deployment Infrastruc-

ture (HDI) container. Right-click on your workspace, and select the View Graph

option.

Figure 11.17 Using the SAP HANA Database Explorer to View Graphs

Your graph will now show in the area on the right in SAP Web IDE for SAP HANA.

You can rearrange the nodes to appear where you want them. When you compare

the graph shown in Figure 11.18, you’ll see that this is exactly what we had in Figure

11.15!

To get the names on the edges (relationships), use the Options icon in the top-right

corner, and set the Edge label.

Tip

The SAP HANA Graph Reference Guide and some blogs refer to an SAP HANA Graph

Viewer that you install via SAP HANA Studio. You don’t need that because this graph

viewer is built in to the SAP HANA database explorer in SAP Web IDE for SAP HANA.


Figure 11.18 Graph Created in SAP HANA (Compare to Figure 11.15)

You can now start analyzing the graph. Click on the < > icon in the top-right corner

to open the dialog box shown in Figure 11.19. You can specify what type of algo-

rithm you want to use when analyzing graphs. The available algorithms are Neigh-

borhood, Shortest Path, and Strongly Connected Components.

In SAP HANA 2.0, we can also specify a “pattern” of vertices and edges, together

with some filters and conditions. We can then use pattern matching to find the

parts of the graphs that match your pattern. You do this by writing some Cypher

code.

The first algorithm is Neighborhood. You specify from which Vertex you want to

start and in which Direction the edges (relationships) must point. You can choose

Outgoing, Incoming, or Any. Finally, you choose the Depth. A minimum depth of

zero includes the starting vertex. A minimum depth of one will exclude the start-

ing vertex and start one relationship level away from this vertex. The maximum

depth specifies how deep you want to search through the graph.


In the first (top) example in Figure 11.19, you see the Outgoing direction for

Michelle. There aren’t many outgoing edges, so the result from the analysis is

small. When you change the Direction to Incoming, the result is much larger. You

can play around by also changing the Direction to Any and changing the value of

the Depth. This simple graph only has a Max Depth of 3.

Figure 11.19 Using the Neighborhood Graph Algorithm


Tip

This graph algorithm only works in the graph viewer when the data type of the Vertexkey column is a string.

The Shortest Path algorithm finds the shortest path between two vertices. Remem-

ber that some edges are like one-way streets. The Direction therefore has a signifi-

cant influence on your results. For example, if we change the direction in Figure

11.20 to Outgoing, we don’t get any result.

You can use this algorithm, for example, to plan your flights. Most people prefer a

direct flight without stopovers. However, if you specify a Weight, like looking at

the cost of the trip, a flight with a stopover might be cheaper. You do this here by

selecting the cost field in your table for the Weight Column.

Figure 11.20 Using the Shortest Path Graph Algorithm

The Strongly Connected Components algorithm finds the vertices that have the

most edges between them and then groups the vertices together on how tightly

they are linked. In Figure 11.21, SAP HANA created five groups. Three of these are

quite obvious from such a small graph, as these are the married couples. The com-

pany is a “group” on its own. It shows each group in a different color.

The last option in the graph viewer is Cypher. This programming language is the

industry standard for graph processing. SAP HANA supports a subset of Cypher for

querying graph data. SAP HANA also has another language from graph processing

called GraphScript. We’ll compare Cypher to GraphScript later in this section.


Figure 11.21 Using the Strongly Connected Components Graph Algorithm

We won’t teach Cypher in this book, but we’ll show you an example so you can get

a feel for it. Listing 11.4 shows the Cypher code for showing the incoming edges to

the SAP vertex. In this case, we could have achieved the same by using the Neigh-

borhood graph algorithm, specifying “SAP” as the Start Vertex, and choosing

Incoming as the Direction. But, of course, you’ll use Cypher for cases where the

other algorithms don’t provide what you’re looking for.

MATCH (a)-[e]->(b)WHERE b.NAME = 'SAP'RETURN a.NAME AS name, e.RELATIONSHIP as relation

Listing 11.4 Cypher Code to Get All Relationships Going to “SAP”

Figure 11.22 shows the result of our Cypher code.

Now that you’ve seen and played with the graph viewer, working with the Graph

node in calculation views will be easy.


Figure 11.22 Using Pattern Matching Cypher Code on a Graph

Graph Nodes in Calculation Views

We discussed the Graph node in Chapter 6. You already saw that the Graph node

must always be the bottom (leaf) node in a calculation view, and you have to

ensure that you keep referential integrity on your data when using graph model-

ing.

The Graph node, as shown in Figure 11.23, uses the graph workspace as its data

source. After you’ve added your graph workspace in the Graph node, you’ll see the

working area as shown on the right side of Figure 11.23. The dropdown menu on the

top-right side shows the same algorithms that you’ve used in the graph viewer.

The form fields are exactly the same as those in the graph viewer. Figure 11.24

shows the available fields for the Neighborhood and Shortest Path graph algo-

rithms. The Graph node has one more option with the Shortest Path graph algo-

rithm. You can also choose to use Shortest Path graph algorithms from a specified

Start Vertex to many other vertices using the One To Many option. This isn’t possi-

ble in the graph viewer as it would have to show multiple graphs graphically on the

screen. The Graph node on the other side returns results in a table, and it’s there-

fore possible to include this option here. You limit the number of vertices on the

One To Many option by specifying the Depth fields.


Figure 11.23 Graph Node in a Calculation View

Figure 11.24 Using the Various Graph Algorithms


The Strongly Connected Components graph algorithm doesn’t have any properties

or form fields.

After you’ve specified some properties for your Graph node, you need to Save and

Build your calculation view. You can then do a Data Preview of the results.

Figure 11.25 shows the Properties area for the Strongly Connected Components

graph algorithm, as well as the result after a build. You’ll notice that the results are

the same as what we got in the graph viewer earlier in Figure 11.21.

Figure 11.25 Strongly Connected Components Graph Algorithm and Results

Tip

It makes sense to use the graph viewer when exploring the possibilities. After you know

what you’re looking for, you can create the Graph node. The Graph node isn’t as conve-

nient for exploring possibilities because you have to do a Save, Build, and Data Previewfor each round. After you know what you need, it’s much better at using the results

together with other business data in a calculation view.

Lastly, we can use the same Cypher code from Listing 11.4 in the Cypher editor of

the Graph node, as shown in Figure 11.26. The results, in table format instead of

graphical, are the same as those shown earlier in Figure 11.22.


Figure 11.26 Pattern Matching Cypher Code and Results

We’ve mentioned that SAP HANA supports both Cypher and GraphScript for que-

rying graph data. Let’s now look at when we use each one.

Cypher and GraphScript

In SAP HANA 1.0, we used Weakly Structured Information Processing and Explora-

tion (WIPE). WIPE is a declarative language, similar to SQL, that allows easy graph-

based queries to be created. As we focus on SAP HANA 2.0, we’ll ignore WIPE.

In SAP HANA 2.0, you can query graph data using graphical calculation views,

Cypher, or GraphScript. We’ve just finished looking at the calculation views.

When do you use Cypher, and when do you use GraphScript?

Cypher is used for pattern matching and graph traversals. It focuses on the what.

For example, you’ll use this to zoom in to one person on LinkedIn. You’ll see what

they studied, where they currently work, their previous jobs, how many languages

they can speak, what courses they attended, and who they are connected to. The

interest is on one vertex and its surrounding vertices.

GraphScript, on the other hand, focuses on the how. It’s used for graph analysis,

where you do topological analysis of the entire graph. This is used, for example, for


planning routes using the shortest path, finding connected components, and

determining page rank.

Table 11.1 lists some differences that will help you decide when to use which lan-

guage.

You’ve already seen the places in the graph viewer and in Graph nodes where you

can use Cypher.

You create a GraphScript procedure the same way that you create a SQLScript pro-

cedure—by creating an .hdbprocedure file. (see Chapter 8 for details.) Instead of

specifying the language as SQLSCRIPT, you set it to GRAPH. Listing 11.5 shows what a

blank GraphScript procedure looks like. Notice that you specify the READS SQL DATA

AS clause like you did with read-only SQLScript procedures. This is obvious from

Table 11.1 when you see that GraphScript is more OLAP style.

PROCEDURE "procedurename"(OUT variable INT)LANGUAGE GRAPH READS SQL DATA AS

BEGIN--Write your GraphScript code hereEND

Listing 11.5 Creating a GraphScript Procedure in an .hdbprocedure File

As with Cypher coding, we won’t teach you GraphScript in this book. (For more

details, read the SAP HANA Graph Reference Guide.) We do, however, give you

some sample code in Listing 11.6 to give you a feel for the GraphScript language.

The BFS mentioned in the following refers to breadth-first search (BFS).

Graph g = Graph("calcStock","GRAPH");ALTER g ADD TEMPORARY VERTEX ATTRIBUTE (Double myStock = 0.0);Multiset<Vertex> myShare = Multiset<Vertex>(:g);Vertex startV = Vertex(:g, :startID);

Cypher GraphScript

Focuses on the what Focuses on the how

Pattern matching, traversal near a specific

start vertex

Graph analysis (of entire graph)

Online transactional processing (OLTP)-

style: short, read and write, high selectivity

Online analytical processing (OLAP)-style:

long and expensive, mostly read, low selec-

tivity

Declarative, query language Imperative, scripting language

Table 11.1 Comparing Cypher and GraphScript


TRAVERSE BFS :g FROM :startVON VISIT EDGE (Edge e, Bigint levels) {Vertex s = SOURCE(:e);Vertex v = TARGET(:e);if (:levels == 0L) { v.myStock = :e.myStock; }else { v.myStock = :v.myStock + :s.myStock; }if (:v.myStock >= 25.0) {

myShare = :myShare UNION {:v};}

};

Listing 11.6 Sample GraphScript Code

SPS 04

In SAP HANA 2.0 SPS 04, the SAP HANA graph engine can now be used directly in SQL sub-

queries using the OPENCYPHER_TABLE function. The information is returned in JSON format.

GraphScript has been expanded in SPS 04 and includes new algorithms. You can now also

use virtual tables with GraphScript, which extends the reach of GraphScript to many

other databases and federated data sources. (See the SAP HANA Smart Data Access sec-

tion in Chapter 9.) You can now also use GraphScript for processing ad hoc graphs on

data structures without having to first create a graph workspace.

Graphs versus Hierarchies

In Chapter 7, we spent a lot of time explaining the hierarchy functionality in SAP

HANA calculation views. In many cases, graphs and hierarchies can look very sim-

ilar. In Table 11.2 is a short summary to help you choose between hierarchies and

graphs.

Hierarchy Graph

Uses standard SQL, ad hoc hierarchies, and

SQL views

Uses domain-specific languages such as

GraphScript and Cypher

Data often in a single table, including a

single edge type (e.g., “parent”)

Needs two tables for vertices and edges

with a clear distinction between them

Uses a graph workspace to link them

together

Few predefined traversal algorithms in SQL Powerful pattern matching queries

Optimized for tree topology—strictly

top-down

Generic graph topology—can have “cyclical”

occurrences

Table 11.2 Differences between Hierarchies and Graphs


Note

You can learn more about SAP HANA graph processing in the SAP HANA Graph Reference

Guide. There is an openSAP course from mid-2018 on graph processing called “Analyzing

Connected Data with SAP HANA Graph” at http://s-prs.co/v487629. And, of course, there

are videos on the topic on YouTube in the SAP HANA Academy channel.

Series Data Modeling

When searching for information on series data, you’ll notice that it’s normally

referred to as “time series” data. SAP HANA uses “series data” because it can also

include any data in a series form, including events and transactions. Time series

data is often associated with the Internet of Things (IoT), where sensors capture

and send out monitoring or event data. However, series data can also include data

such as database log files.

In the database world, interest in the topic of time series databases has seen the

most growth of all types of databases over the past two years. At http://s-prs.co/

v487630, you can scroll down the page to see the Trend of the last 24 months. Time

series databases have clearly seen the fastest growth in interest. Many new data-

bases were released into the market that focus exclusively on time series data.

Many of them basically store the data using a label (or “tag”) with key–value pairs.

This is typical for many NoSQL databases.

We’ll just provide an overview on the topic. You can combine series data with

aggregation, analysis, and predictive modeling to create valuable insights into

real-world operations.

Series Data in Everyday Life

Let’s say you want to keep track of your weight. Every morning after waking up,

you joyfully jump on the bathroom scale and record your weight. After a while, the

data will look something like that shown in Table 11.3.

You’ve created a time series. The series data is a sequence of regular measurements

taken over a period of time.





There are only three columns, but you’ll have hundreds of records in just a few

months, resulting in a very long, thin weight table over time. SAP HANA is great for

such tables because it’s column-based by default and also uses lossless data com-

pression. For time series, SAP HANA can use additional compression methods to

reduce the storage requirements even further.

Series data uses the following various columns:

� Profile column

This is the sensor or object that you’re collecting data from. In this case, it’s the

name of the person. You can also add other members of the family to the table.

� Series column

This column is the time interval for the data collection. Here you’re recording

your weight daily.

� Value columns

The value in this table is your weight on that specific day. In SAP HANA, you can

have multiple value columns; for example, you can also record how many

hours you slept that night or your waist circumference.

Let’s look at how SAP HANA can be used with series data.

If you think about it, most data is series data. In a database, transactions are a

series of data points. Many times, these transactions are recorded with a time

stamp (date and time) field. In a database, all changes to the data are recorded in

database log files, where each change is recorded with a time stamp. Log files are

therefore series data. Log files record the changes to our data and are everywhere.

Person Date1 Weight

You 1 January 176 lbs



You 4 January 173.3 lbs

You … …

You 14 February 159 lbs

You 15 February 160 lbs

Table 11.3 Tracking Your Weight


Tip

Because we’re working with time stamps, it’s recommended to store them using a single

standard, for example, UTC or UTC with a constant offset. Changes to daylight savings or

differences in time zones between countries can introduce complexities when combining

series data with regular business data.

As series data looks similar to normal data, you could use normal tables for series

data. But sometimes the workload for series data isn’t exactly the same as a nor-

mal transactional workload. The volumes of data are normally also much larger.

Hence, we need some special-purpose optimized features for managing series

data. SAP HANA provides such features.

Examples of Series Data

Before diving into the details, let’s look at some use cases for series data and why

companies are so interested in this data. In the previous Real-World Scenario box,

you already saw one such use case.

With the rise of sensors and IoT, the amount of generated data is increasing dra-

matically. For example, many cities around the world have installed smart meters

at each home. In cities like Johannesburg, South Africa, you could have 1 million

smart meters for electricity or water. These smart meters generate a reading every

15 minutes. Each meter therefore creates 2,920 readings per year. For 1 million

meters, there are 2.9 billion readings per year! With such volumes of data, efficient

storage is vital.

The reason that cities are interested in such data is that they can analyze it to see

usage patterns and make predictions for planning infrastructure and providing

services for the population. With readings every 15 minutes, they can deduce what

devices were used in each 15-minute interval. Assume a stove uses 500 W in 15

minutes, a hot water heater uses 250 W in 15 minutes, a TV uses 25 W, and an LED

lightbulb uses 2 W. If they get a reading of 29 W in the past 15 minutes, they can

deduce that this home probably had a TV and two lights on. If they get a reading of

512 W, the people were probably cooking and had a few lights on. The city then

aggregates this data to see when people wake up, shower, and have breakfast. They

use this to predict traffic patterns and electricity demand, see the effects of rain

and cold weather, and so on.

Another use case is seen in vehicles. All modern cars have a slot for an On-Board

Diagnostics (OBD2) device. You can buy a fairly cheap device and plug it into your


car to see the data from your car, for example, speed and fuel consumption, as well

as diagnostic and warning messages. As an exercise, you can build a tool in SAP

HANA to analyze your driving, and use this information to decrease your fuel con-

sumption.

With the move to cloud and microservices architectures, the field of DevOps is

growing daily. DevOps is a software development methodology that combines

software development (Dev) with operations (Ops). DevOps aims to automate

many of the processes in the software development cycle, reducing the time to

market for new services. To ensure service uptime and optimal performance, you

need to constantly monitor these microservices. Analyzing log files has become so

important that new time series databases, such as Prometheus, have sprung up to

address this need.

Other use cases are daily currency conversion rates, share prices, and rainfall per

month.

As you can see, series data allows you to collect large volumes of data, analyze the

way this data behaves over time, and use the information for patterns, trends, and

forecasts. The fields of engineering, technology, electronics, development, opera-

tions, statistics, signal processing, and machine learning are coming together

when we work with series data.

Note

It’s important to realize that data in the real world isn’t always perfect or complete. You’ll

always get some missing readings or some abnormal readings due to network errors,

faulty sensors, buffer overflows, bad code, or myriad other reasons.

Internet of Things, Edge Processing, and Equidistant Series Data

We started looking at IoT, smart meters, and OBD2 devices in the previous section.

To better understand IoT, we can also look at a common IoT scenario—exercise

and smart watches. As you move, run, or exercise, the watch saves your GPS loca-

tion, the elevation, and your heart rate every second.

So how does the watch know what your heart rate is at any moment of time? The

electronics of the watch actually detect the individual heartbeats and measure the

time between the heartbeats. The top graph in Figure 11.27 illustrates resting heart-

beats. The time between heartbeats, shown as t1, is relatively long—just over 1 sec-

ond. The bottom graph in Figure 11.27 illustrates heartbeats during exercise, and

the time (t2) is much shorter.


Figure 11.27 Heart Rates Measured by a Smart Watch

The watch now counts the number of heartbeats in a time period to calculate your

heart rate. If it measures 20 heartbeats in 15 seconds, it calculates that this would

result in 80 heartbeats per minute, and thus your heart rate is 80 bpm (beats per

minute).

The important thing to realize is that almost all IoT devices do some calculations

on the data. This is called IoT edge processing.

The watch processes your heartbeat data, averages it over the past few seconds,

calculates your heart rate, and displays this average to you every second. The

watch doesn’t store the “raw” data, which is, in this case, the time at which each

heartbeat occurs. Rather, the watch stores the calculated value of your heart rate

each second.

The series data that the watch provides every second is called equidistant. Equidis-

tant is when the time increments between successive records in the series data are

a constant—in this case, 1 second. When you look at the heartbeat data that the

watch actually measured (refer to Figure 11.27), you’ll see that t1 and t2 aren’t the

same. Using edge processing, the watch changed the nonequidistant heartbeat

series data into equidistant heart rate series data.

The heartbeat data is showing the actual events, that is, the exact time when each

heartbeat occurred. The heart rate data, on the other side, shows aggregate sum-

maries at regular intervals of 1 second. From this, you see that recording actual

event data provides nonequidistant series data. Aggregated data normally pro-

vides equidistant series data. For example, actual financial transactions (events)

are nonequidistant, whereas daily or monthly balances (aggregates) are equidis-

tant.

t1

t2Heart rate at 150 bpm

Heart rate at 50 bpm (beats per minute)


It’s not just edge processing that performs aggregation of series data. You can also

aggregate series data in SAP HANA, as discussed later in this appendix.

SAP HANA can also convert nonequidistant series data into equidistant data.

Using series data functions, you can specify which rounding rules you want to

apply. For example, you can round 8:30 down to 8:00 or up to 9:00.

You might be concerned that you’re losing some data when IoT devices are using

aggregations. Most of the time, this isn’t a concern because you normally don’t

want that much data. When exercising, you don’t want to see each heartbeat. The

aggregated heart rate information is exactly what you need at that point. If, how-

ever, you’re a medical practitioner, you might be interested in the actual heart-

beats, for example, to detect irregular heartbeats or atrial fibrillation.

In manufacturing plants, “historian” databases are used to store the IoT series

data. These databases can then be used later to do predictive maintenance using

predictive algorithms like those we discussed in Chapter 10.

Note

IoT and series data are very often combined with streaming data, predictive modeling,

and machine learning. Everything you learned in Chapter 10 and this chapter is used

together in powerful ways to create business insights. SAP HANA provides everything you

need for addressing these requirements.

SAP HANA for Series Data

Due to the increased popularity of time series databases, quite a few of these new

databases have appeared in the market. As the market is maturing, users of these

time series databases run into some limitations; for example, some of them only

allow a single value field (like Table 11.3). This is due to the key–value pair structure

they received from their NoSQL heritage. You have to create another new table to

add a new value column. In SAP HANA, you can have multiple value columns—for

example, you can also record how many hours you slept that night or your waist

circumference—all in the same table.

One of the biggest limitations of dedicated time series databases is that they can

only store time series data. SAP HANA databases can contain both series data and

regular business data. From a modeling perspective, there is no difference be-

tween these different types of database tables. You can integrate series data with

any other type of data to produce powerful applications and analytics by using


calculation views or standard SQL statements to query any table, including series

data tables.

Many of the dedicated time series databases use their own limited query lan-

guages with SQL-like syntax that just introduces mental friction. Existing SQL-

based tools don’t work with these databases. SAP HANA series data can be accessed

using standard SQL, in which simple queries stay simple, complex queries are

powerful, and joins are easy. Data modelers and business users can get started

immediately using familiar business intelligence (BI) tools, using legacy systems

to query this data, and inheriting a vast ecosystem of functionality (e.g., streaming

data; extract, transform, load (ETL); enterprise resource planning (ERP); BI; visual-

ization; etc.).

Although there is no difference in using a series table in SAP HANA from a model-

ing perspective, technically, there is obviously a difference between series data

tables and regular column tables. You’ve seen that series data can have large vol-

umes and that the tables tend to be long and thin. SAP HANA can use additional

compression methods to reduce the storage requirements even further.

In Table 11.3 previously, you saw a series data table. While you can store this data in

a normal column table in SAP HANA, which will already provide quite good com-

pression, you can do even better. Instead of actually storing the series (Date1) col-

umn, you can calculate these dates. The table is an equidistant series data table,

meaning that the time period between dates of successive records is a constant.

SAP HANA only has to store the start date and the equidistant time period—

instead of the entire column! To calculate the date for the fourth record, SAP

HANA takes the start date (1 January) and adds three days to give you 4 January.

The formula for calculating is therefore as follows:

Calculated time stamp = Starting time stamp + (Record number – 1) × Equidistant

time period

SAP HANA does this compression automatically in the background when you use

an equidistant series table, so you won’t see the compression or the calculations.

You can use calculation views and normal SQL statements, as if the data is actually

stored in the table instead of being calculated.

You can find out how to calculate how much space you save when using an equi-

distant series table instead of a column table by watching the SAP HANA Academy

videos about series data on YouTube (http://s-prs.co/v487605). In the specific case

given in these videos, they only used about 12% of the space. The source code is at

http://s-prs.co/v487631.




Note

Calculation views or SQL queries that use compressed equidistant (calculated) series col-

umns will be slower than if the data were stored in nonequidistant tables. You should use

nonequidistant tables if your series column (time) data has no pattern or when trading

better performance for more memory is preferred and acceptable.

Sometimes, when you keep adding a lot of data to a series table over time, the per-

formance might decrease. You can then run an ALTER TABLE SERIES REORGANIZE state-

ment to improve the compression.

Creating Series Tables

Let’s say you want to create the series table shown earlier in Table 11.3. You can do

so using Listing 11.7.

CREATE COLUMN TABLE SCHEMA_NAME.TABLE_NAME (Person VARCHAR(25) NOT NULL,Date1 DATE NOT NULL,Weight DECIMAL(5,2),PRIMARY KEY (Person, Date1)

)SERIES (

SERIES KEY(Person)PERIOD FOR SERIES(Date1)EQUIDISTANT INCREMENT BY INTERVAL 1 DAYMINVALUE '2019-01-01'MAXVALUE '2019-12-31’

);

Listing 11.7 Creating the Series Table for Table 11.3

You’ll notice that this code starts with creating a normal column table, but then

adds the SERIES clause to specify that this is a series table. Inside this SERIES clause,

note the following:

� The SERIES KEY is used to define the Person profile column. In this case, it’s the

name of the person whose weight is measured. The SERIES KEY can be multiple

columns.

� PERIOD FOR SERIES is used to specify the Date1 series column, where the time val-

ues are stored.

� EQUIDISTANT INCREMENT BY defines that this series is equidistant, and the distance

between data points is one day.

� MINVALUE and MAXVALUE specifies the range of acceptable dates for the Date1 series

column. MINVALUE specifies the start point, and MAXVALUE specifies the end point.


SAP HANA will also create a trigger for the table, which will return errors if you try

to insert dates that aren’t in the specified range or if the date doesn’t align with the

daily interval (in this case).

You can use the SERIES_GENERATE function to prepopulate the entire series column

(Date1). The rows are then ready for just adding values (Weight) at a later point.

You can alter a series table using the ALTER TABLE statement. However, the SERIES

clause of an existing series table can’t be altered.

Horizontal and Vertical Aggregation

A challenge you’ll often face when working with series tables is how to JOIN two

series tables with different time intervals. To solve this, you must use horizontal

aggregation.

Horizontal aggregation is when you take granular data and aggregate it to a less

granular level. For example, you can aggregate hourly data to a daily level. Or you

can disaggregate the data to a more granular level, for example, breaking up

hourly data to 15-minute intervals.

Note

Horizontal aggregation doesn’t change the data in the series table. It’s merely a “view”

that displays the data at a higher or lower level of granularity and allows you to JOINseries tables with different time intervals.

You can use normal aggregation functions, such as SUM and AVG, for aggregating the

value columns. When taking the Date1 series column, for example, to a less granu-

lar level, you use the SERIES_ROUND function. In Listing 11.8, horizontal aggregation

is applied to the data from Table 11.3. In this case, you use the ROUND_DOWN option,

meaning that 14 February gets rounded down to the month of February, rather

than rounded up to the month of March. In this case, this is quite obvious, but,

sometimes, you’ll want 8:59 to be rounded up to 9:00, rather than rounded down

to 8:00.

SELECTPerson,SERIES_ROUND(Date1, 'INTERVAL 1 MONTH', ROUND_DOWN) AS Monthly,AVG(Weight) AS MonthlyWeight

FROM SCHEMA_NAME.TABLE_NAMEGROUP BY Person, SERIES_ROUND(Date1, 'INTERVAL 1 MONTH', ROUND_DOWN);

Listing 11.8 Horizontal Aggregation


Listing 11.8 is the classical SQL format. You might not like the fact that the SERIES_

ROUND function is called twice. Listing 11.9 shows an alternative way of accomplish-

ing the same horizontal aggregation by using a WITH … SELECT statement, which

only calls the SERIES_ROUND function once.

WITH TMP AS (SELECT Person,SERIES_ROUND(Date1, 'INTERVAL 1 MONTH', ROUND_DOWN) AS Monthly,WeightFROM SCHEMA_NAME.TABLE_NAME

)SELECT Person, TIMER_HOUR, AVG(Weight) AS MonthlyWeightFROM TMPGROUP BY Person, Monthly;

Listing 11.9 Horizontal Aggregation Using a WITH Clause

You use the SERIES_DISAGGREGATE function instead of the SERIES_ROUND function

when you move from coarse units (e.g., daily) to finer units (e.g., hourly).

Vertical aggregation is when you aggregate measures over a number of attributes.

You can JOIN a series table with other tables in SAP HANA and aggregate the results

to create interesting reports, for example, per factory, per region, per machine

type, or per year.

All logical join types are supported when joining two series tables. However, both

series tables must be at similar levels of granularity. Both can be nonequidistant or

equidistant with the same time interval.

Functions for Series Data

Special SQL functions are available specifically for series data. You’ve seen quite a

few of these already, for example, SERIES_ROUND, SERIES_DISAGGREGATE, and SERIES_

GENERATE. These functions are documented in the SAP HANA Series Data Developer

Guide at http://s-prs.co/v487620 in the Development section.

More advanced functions, such as CUBIC_SPLINE_APPROX, allow you to fit a cubic

spline over your series data. You also have additional analytic functions for analyz-

ing series data, such as MEDIAN to compute a median value or CORR to calculate the

correlation coefficient.




In this chapter, we looked at the following important terms:

� Spatial processing

Processing that uses data to describe, manipulate, and calculate the position,

shape, and orientation of objects in a defined space.

� Spatial reference systems (SRSs)

Specifications for various flat-earth and round-earth models.

� Spatial joins and predicates

The way that spatial shapes and points can be related to each other.

� Spatial functions

Special OGC-compliant functions to process spatial data in the system.

� Geocoding

The process of converting address information into geographic locations, such

as latitude and longitude.

� Graph processing

Used to process relationship and attributes of highly networked entities.

� Vertices

Nodes in a graph that are related to each other.

� Edge

Relationships between vertices in a graph.

� Graph workspace

File linking the vertex data used in a graph to the edge data.

� Time series data

A sequence of regular measurements taken over a period of time.

� Equidistant

Refers to when the time increments between successive records in the series

data are constant, for example, one hour.

� Horizontal aggregation

Taking granular data and aggregating it to a less granular level. For example,

you can aggregate hourly data to a daily level or disaggregate the data to a more

granular level, such as breaking up hourly data to 15-minute intervals.


Practice Questions









1. What spatial data type would you use to store multiple shop locations?

� A. ST_Point

� B. ST_MultiPoint

� C. ST_Polygon

� D. ST_MultiLineString

2. Which spatial data formats can you import into SAP HANA? (There are 2 cor-

rect answers.)

� A. Scalable Vector Graphic (SVG)

� B. Well-Known Text (WKT)

� C. ESRI shapefiles

� D. GeoJSON

3. Which modeling features can you use in flowgraphs? (There are 2 correct

answers.)

� A. Text

� B. Predictive

� C. Spatial

� D. Graph


4. SAP HANA spatial functions are compliant to the standards set by which com-

pany?

� A. OGC

� B. ESRI

� C. W3C

� D. HERE

5. What characteristics does the WGS84 (planar) spatial reference system (SRS)

have? (There are 2 correct answers.)

� A. It’s a flat-earth type SRS,

� B. It uses a geographic coordinate system.

� C. It’s a round-earth type SRS.

� D. It uses a Cartesian coordinate system.

6. What is the default spatial reference system identity (SRID) for SAP HANA?

� A. 0 (2D planar)

� B. 4326 (WGS84)

� C. 3857 (Web Mercator)

7. What is the m-coordinate in a spatial point used for?

� A. The height above or below sea level

� B. Measures, such as wind speed

� C. The latitude

� D. The longitude

8. A spatial point is on the boundary of a shape. Which predicate do you use to

accept it as enclosed by the shape?

� A. ST_Contains

� B. ST_Within

� C. ST_Covers


9. What function do you use to create a polygon that encloses a cluster of spatial

points?

� A. ST_UnionAggr

� B. ST_ConvexHullAggr

� C. ST_EnvelopeAggr

10. You need to cluster a large number of spatial data points to get a quick idea of

the data, and you don’t care about visually appealing results. What SAP HANA

spatial clustering algorithm do you use?

� A. Hexagonal

� B. K-means

� C. Grid

� D. DBSCAN

11. What does the graph workspace file specify? (There are 2 correct answers.)

� A. The definition of the graph model

� B. The attributes available in the graph model

� C. How the vertex and edge table are related

� D. The graph algorithm used by the Graph node

12. True or False: The graph workspace file is an .hdbcds file.

� A. True

� B. False

13. Which of the following can you use in the graph viewer in the SAP HANA data-

base explorer? (There are 2 correct answers.)

� A. Cypher

� B. GraphScript

� C. The Shortest Path One to Many algorithm

� D. The Strongly Connected Components algorithm


14. Which graph algorithm do you use to find the cheapest route to a specified

destination?

� A. Strongly Connected Components

� B. Shortest Path One to Many

� C. Neighborhood

� D. Shortest Path One to One

15. Which property do you specify for the graph algorithm to find the cheapest

route to a specified destination?

� A. Direction

� B. Weight

� C. Minimum Depth

� D. Maximum Depth

16. True or False: The Graph node must be the bottom node in a cube calculation

view.

� A. True

� B. False

17. You need to use a Graph node with a shortest path algorithm in a calculation

view, and require the end users to only specify the start vertex at runtime.

What is the easiest way to achieve this?

� A. Use a GraphScript procedure.

� B. Write an SAP HANA XSA application.

� C. Use input parameters.

� D. Use a REST API call.

18. Bonus Question: Which language do you use when you need to use a tree-like

topology with a declarative query language?

� A. Cypher

� B. SQLScript

� C. GraphScript


19. What are the vertices in a graph model?

� A. The nodes themselves

� B. The relationships between the nodes

� C. The attributes of the nodes

20. What will you use to convert daily series data to an hourly level?

Note: There are 2 correct answers to this question.

� A. Vertical aggregation

� B. Horizontal aggregation

� C. SERIES_DISAGGREGATE

� D. SERIES_ROUND

21. What do you use to create an equidistant series table?

� A. CREATE TABLE

� B. CREATE SERIES TABLE

� C. CREATE COLUMN TABLE

� D. CREATE HISTORY COLUMN TABLE

22. You create an equidistant series table. In the SERIES clause, what do use to

identify the profile column?

� A. SERIES KEY

� B. PERIOD FOR SERIES

� C. EQUIDISTANT INCREMENT BY

� D. PRIMARY KEY



Use a ST_MultiPoint data type to store multiple shop locations. ST_Point can

only store a single shop location, and ST_Polygon and ST_MultiLineString can’t

be used for shop locations.



You can import Well-Known Text (WKT) and ESRI shapefiles into SAP HANA.

You can only export Scalable Vector Graphic (SVG) and GeoJSON file formats.


Predictive and spatial modeling can be done in flowgraphs.


The SAP HANA spatial functions are compliant to the standards set by OGC.

ESRI is the largest spatial software vendor and sets the de facto standard for

shape files.

The W3C looks after the standard for SVG (and other web standards)

HERE provides maps.


The WGS84 (planar) spatial reference system (SRS) is a flat-earth type SRS and

uses a Cartesian coordinate system.

If you got this wrong, it’s because it was a trick question. The WGS84 (planar)

SRS is NOT the same as the WGS84 SRS. Check the information below Figure 11.1.


The default spatial reference system identity (SRID) for SAP HANA is 0 (2D pla-

nar).

Many times, you should rather use 4326 (WGS84).


The m-coordinate is a spatial point used for measures such as wind speed. (M is

for measure.)


You use ST_Covers to accept that a spatial point is on the boundary of a shape as

enclosed by the shape.

ST_Contains and ST_Within don’t show a point on the boundary of a shape as

being enclosed by the shape.


You use the ST_ConvexHullAggr function to create a polygon that encloses a clus-

ter of spatial points.

The ST_EnvelopeAggr function returns a rectangle, which technically is also a

polygon. This is therefore a bad question, as someone could argue about the

answer, and would thus not occur in the real exam.



You use the SAP HANA grid spatial clustering algorithm to cluster a large num-

ber of spatial data points to get a quick idea of the data or when you don’t care

about visually appealing results.

The hexagonal spatial clustering algorithm (in SPS 04) might create more visu-

ally appealing results.

The k-means and DBSCAN spatial clustering algorithms aren’t as quick as the

grid spatial clustering algorithm.


The graph workspace file specifies the definition of the graph model and how

the vertex and edge table are related.

The attributes available in the graph model are just the additional fields avail-

able in the tables or views.


False: The graph workspace file is NOT an .hdbcds file but an .hdbworkspace file.


You use Cypher and the Strongly Connected Components algorithm in the

graph viewer in the SAP HANA database explorer.

GraphScript uses an .hdbprocedure file.

The Shortest Path One to Many algorithm is available in the Graph node but

NOT in the graph viewer.


You use the Shortest Path One to One graph algorithm to find the cheapest

route to a specified destination.

The Shortest Path One to Many graph algorithm would also be correct if the

question did not say “specified destination,” as you could generate all routes

and pick the cheapest one for your destination.


You specify the Weight Column property for the graph algorithm to find the

cheapest route to a specified destination.


True: The graph node must be the bottom node in a cube calculation view and,

actually, for all calculation views.



When you need to use a Graph node with a Shortest Path algorithm in a calcu-

lation view and require the end users to only specify the start vertex at runtime,

you simply use input parameters (refer to Figure 11.23).

Writing an SAP HANA XSA application could work, but not in all cases; for

example, it won’t work when you want the users to specify the start vertex

while they are using SAP Analytics Cloud.


You use SQLScript when you need to use a tree-like topology with a declarative

query language. The tree-like topology refers to hierarchies.

Cypher is a declarative query language, but it’s used for generic graph topology.

This question is too detailed and hard, which is why it was marked as a bonus

question. If you could answer it correctly, you know the topic very well!


The vertices in a graph model are the nodes.

The relationships between the nodes are called edges.

20. Correct answer: B, C

You use horizontal aggregation and SERIES_DISAGGREGATE to convert daily series

data to an hourly level. In this case, you go to hourly, which means there are

more values. You need to disaggregate to achieve this.

SERIES_ROUND is used with aggregation. Aggregation (e.g., SUM) takes many values

and reduces them to one value.


You use CREATE COLUMN TABLE with a SERIES clause to create a series table.

There is no CREATE SERIES TABLE statement.

CREATE TABLE creates a row table.

A history column table is for audit log type tables, where you keep a full history

of all changes made to the table.


You identify the profile column, which is the name of the “sensor” in IoT, with

SERIES KEY.

The PERIOD FOR SERIES (used on the series column) is used for the time period.

Note

Some questions in the certification exam will only have three answer options.


Takeaway

You now understand the key components of spatial processing, which spatial data

types are available, and how to perform spatial joins in SAP HANA information

views.

You also know how to work with nodes (vertices) and relationships (edges) in a

graph, as well as how to combine them using a graph workspace. You’ve used the

graph viewer in the SAP HANA database explorer and used Graph nodes in graphi-

cal calculation views. And you know the differences between Cypher and Graph-

Script, and hierarchies and graphs.

Finally, you learned how to create a series table, understood what an equidistant

series is, and gained a basic understanding of series data modeling techniques.

Summary

We discussed how to use spatial processing, how to combine business data with

maps, and how to easily do complex queries on networked entities.

In the same way, we can combine graph data with business data to create powerful

queries that can’t be solved using traditional hierarchies and reporting methods.

We discussed important aspects of SAP HANA series data, including the advan-

tages of using SAP HANA. Throughout this overview of SAP HANA series data,

you’ve learned more about the SAP HANA series data functionality, as well as the

basic concepts of series data in SAP HANA, such as series tables, equidistant series

data, and horizontal aggregation.

In the next chapter, we’ll look at how to improve SAP HANA information models

to achieve optimal performance.

Chapter 12

Optimization of Information Models


� Understand the performance enhancements in SAP HANA

� Get to know techniques for optimizing performance

� Learn how to use the SAP HANA optimization tools

� Implement good modeling practices for optimal performance

� Create optimal SQL and SQLScript

Chapter 12 Optimization of Information Models520

During our exploration of SAP HANA, we’ve frequently come across the topic of

performance. SAP HANA is well known for the improvements it brings to business

process runtimes and its real-time computing abilities.

In this chapter, we’ll discuss some of these performance enhancements that are

built in to SAP HANA and review the techniques you’ve learned previously. We’ll

then look into new ways of pinpointing performance issues and discuss how to

use the optimization tools built in to SAP HANA. Finally, we’ll examine some

guidelines and best practices when modeling and writing SQL and SQLScript code.

Real-World Scenario

The CEO of your company reads a 2009 study by Akamai stating that 40% of

online shoppers will abandon a website that takes longer than three seconds

to load.

Therefore, you’re tasked with helping to achieve a response time of less than

three seconds for most reports and analytics that sales representatives use.

Many such users work on tablets, especially when they are visiting custom-

ers. You decide to use a mobile-first approach, creating interactive analytics

and reports to reduce network requirements.

Some of the reports currently take more than two minutes to run on SAP

HANA. Using your knowledge of optimization, you manage to bring these

times down to just a few seconds.



knowledge of the SAP HANA modeling tools.


topics:

� Performance enhancements in SAP HANA

� Techniques for optimizing performance

� Using the performance analysis mode, debug mode, and other optimization

tools

� Good modeling practices for optimal performance

� Creating optimal SQL and SQLScript


Note



In the preceding chapters, we’ve discussed many performance enhancements that

are built-in to the design of SAP HANA; in this chapter, we’ll walk through various

optimization topics in SAP HANA.

Architecture and Performance

The largest and most fundamental change that comes with SAP HANA technology

is the move from disks to memory. The in-memory technology that SAP HANA is

built on has been one of the biggest contributors of performance improvements.

The decision to break with the legacy of disk-based systems and use in-memory

systems as the central focus point (and everything that comes with this) has led to

significant changes in performance.

The next step was to combine memory and multicore CPUs. If we store everything

in memory and have plenty of processing power, we don’t have to store calculated

results. We just recalculate them when we need them, which opens the door to

real-time computing. Real-time computing requires that results be recalculated

each time they’re needed because the data might have changed since the last

query.

This combination of memory and CPU processing power led to new innovations.

Now, you can combine online transactional processing (OLTP) and online analyti-

cal processing (OLAP) together in the same system: the OLAP components don’t

need the storage of precalculated cubes and can be recalculated without impacting

the performance of the OLTP system.

Another innovation prompted by combining memory and CPU is data compres-

sion. This both reduces memory requirements and helps to better utilize CPUs by

loading more data in the level 1 CPU cache. Loading more data into CPUs once

again improves performance.

To improve compression, SAP HANA prefers columnar storage. Along with many

advantages, columnar storage also has a few disadvantages: inserting new records

and updating existing records aren’t handled as efficiently in column tables as in


row tables. To solve this, SAP HANA implemented the insert-only principle with

delta buffers. We use row storage for delta buffers to improve insert speeds and

then use delta merges to bring the data back to columnar storage, which leads to

better read performance.

Tip

The performance improvements in SAP HANA don’t mean that we can be negligent in

modeling and development. For example, you shouldn’t use SELECT * statements or

choose any unnecessary fields in queries because doing so decreases the efficiency of the

column storage.

Using multicore processors, everything in SAP HANA was designed from the

ground up to run in parallel. Even a single aggregation value is calculated using

parallelism. This led to SAP HANA growing from a single hardware appliance to a

distributed scale-out system that can work across multiple server nodes and

finally to multitenant database containers (MDCs). Tables can now also be parti-

tioned across multiple servers. With more servers, the scale and performance of

SAP HANA systems has vastly advanced.

Redesigned and Optimized Applications

While combining memory and processors, we also saw that moving data from

memory to application servers isn’t the optimal way to work with an in-memory

database. It makes more sense to push down some of the application logic to the

SAP HANA system for fast access to the data in memory and plenty of computing

resources for calculations.

SAP started by making SAP HANA available as a side-by-side accelerator solution

to improve issues with long-running processes in current systems. Over time, SAP

business applications were migrated to SAP HANA and reengineered to make use

of the advantages it offers.

With SAP S/4HANA, we now have a system that uses views instead of materialized

aggregates in lookup tables, significantly reducing the size of the system, simplify-

ing the code, and enriching the user experience.


Effects of Good Performance

We sometimes hear the following: “Why do we have to optimize our SAP HANA

models that much? Surely five minutes for an analysis, a report, or some screen is

good enough.” Yes, sometimes a response time of five minutes is good enough.

But this means people focus only on the task at hand. They don’t look at what is

possible and what they’re currently not doing.

What other things, which were previously impossible, can we do now if this cur-

rent process runs in less than a second? What other data can we combine with the

current process that unlocks more business value? How can we change our busi-

ness because of this?

Example

Most people see SAP HANA as just some technology. Some people see SAP HANA as an

enabler. Few people view SAP HANA as a “change agent.” The history of SAP over the past

few years is proof of how SAP HANA can change a business.

When SAP HANA was released, there was a lot of focus on how it could be used to

accelerate reports and analytics. At some stage, people realized that they could not

only process the same amount of data faster, but they could now process more

data. SAP started using SAP HANA for big data scenarios.

One of the major sources of big data is sensors or what is called the Internet of

Things (IoT). Many of these “things” (sensors) are sending a constant stream of

data out, which results in large data sets that keep growing. (We look at streaming

data in Chapter 9.) SAP evolved this focus on the IoT into its new SAP Leonardo

portfolio.

When you work with big data, you start with historical analysis. But if the perfor-

mance is great, you can start looking at using this historical analysis to predict

events. (We discussed the predictive capabilities of SAP HANA in Chapter 10.) SAP

now has the SAP Predictive Maintenance and Service system that uses historical

IoT data from machines to predict the optimal time to service, maintain, and

upgrade these machines, as well as exactly which parts need to be replaced at what

point in time. This results in huge savings.

The next step from predictive is machine learning. Some people now call some

things machine learning that are still really predictive. Machine learning can build

on predictive, but it also has elements such as neural networks. SAP HANA has a


machine learning component, which is even available for SAP HANA, express edi-

tion. Machine learning with SAP HANA is also part of the new SAP Leonardo port-

folio.

Look at the user’s experience. If a system takes five minutes to respond, you can’t

really use it on a mobile device. We started the chapter with a study that showed

people expected answers in less than three seconds on mobile. More than half the

requests on the Internet are now coming from mobile devices. A response time of

five minutes isn’t good enough in this case.

Lastly, when system responses are fast, you can start moving these systems to the

cloud. People can now access these systems from any device and from anywhere.

SAP now has a huge focus on cloud systems. Many of these would never have

worked if the responses to users were slow. SAP Cloud Platform is now a central

component in the SAP Leonardo portfolio and the new “Intelligent Enterprise.”

Without good performance and a focus on optimizing their modeling and devel-

opment, products such as SAP Leonardo and SAP Cloud Platform might never

have been conceived. Performance can open doors to new business models.

Let’s see what we’ve learned already to help us with optimizing our information

models.

Information Modeling Techniques

In previous chapters, we discussed some techniques that can help optimize infor-

mation models, such as the following:

� Limit data as quickly as possible by doing the following:

– Define filters on the source data.

– Apply variables in your SAP HANA views to push down filters at runtime.

– Select only the fields that you require in your query results. Don’t use SELECT *.

– Let SAP HANA perform the calculations and send only the calculated results

to a business application, instead of sending lots of data to the application

and performing the calculations there.

– Don’t output your data as granular (at record level) in a calculation view

when your analysis displays it as aggregates.

� Speed up filters by partitioning the tables that you filter on.

� Perform calculations as late as possible. For example, perform aggregation

before calculation where possible.


� Use referential joins when your data has referential integrity. When working

with referential integrity, the SAP HANA system doesn’t have to evaluate the

join for all queries.

� Similarly, use dynamic joins to optimize the join performance for certain que-

ries.

� Join tables on key fields or indexed columns.

� Use a union instead of a join for combining large sets of data.

� Make unions even more efficient by using union pruning. We discussed this in

Chapter 6 already, when discussing unions.

� Where possible, use a single fact table in a data foundation of a star join view.

� Build up your information views in layers. This helps SAP HANA to process the

requests in parallel, which drastically improves performance.

If you use these techniques, you’ll avoid many optimization hazards. These tech-

niques should be part of your everyday workflow.

The best optimization is not having to optimize. By avoiding the pitfalls in this

chapter, you’ll most likely not need to optimize your modeling later. In spite of

your best efforts, however, sometimes you need to optimize an information

model.

Optimization Tools

When optimizing an SAP HANA information model, a number of tools are at your

disposal. You need to know when to pick which optimization tool and how to use

it.

Note

If you’ve worked with SAP HANA 1.0 before, you’ll notice some of the tools that you once

knew have changed. We won’t discuss SAP HANA database engines, the Explain Plan tool,

the Visualize Plan tool, or the Administration Perspective. These were available in SAP

HANA 1.0 and SAP HANA Studio. Instead, we’ll focus on the new tools found in SAP Web

IDE for SAP HANA.


Performance Analysis Mode

Performance analysis mode is available from the top toolbar when you build SAP

HANA calculation views. You can switch this mode on or off when designing infor-

mation views. The tool will analyze your view and suggest possible improvements.

When you select a node—for example, the Join node—in your view, and then open

the Performance Analysis tab (top right), you might see some improvement recom-

mendations from the SAP HANA system. You’ll notice that a new tab, Performance

Analysis, has been added to the right side of the editor in which you design the

information view.

Figure 12.1 shows the Product calculation view from the SAP HANA Interactive Edu-

cation (SHINE) content. We select the first (bottom) node in the view, and see the

Performance Analysis of this Join node.

Figure 12.1 Performance Analysis Mode Optimization Suggestions


The first section includes the Join Type and Cardinality of the join. The next section

includes the Partition Type and Number of Rows of each of the tables in the join. If

we have many rows in a table, SAP HANA might suggest that we partition the table

to improve performance. In this case, the BusinessPartner table is already parti-

tioned using a Hash partition.

There are two possible improvement suggestions in Figure 12.1:

� Proposed cardinality

In this case, we didn’t specify any cardinality on the join. SAP HANA analyzed

the tables and the Join node and suggested that a n..1 cardinality should be set.

Setting the cardinality will allow SAP HANA to execute the view faster because

it won’t have to calculate what the cardinality is first. The suggestion of n..1 car-

dinality makes sense because we’ll order multiple products (left table) from

each supplier (right table). A message stating Selected cardinality does not

match the proposed cardinality is shown below the Cardinality field.

� Join type

SAP HANA looked at the data to see if referential integrity was ensured. Because

Referential Integrity is Maintained in this case, we can use a referential join

instead of the inner join type in this Join node.

In the SAP Web IDE for SAP HANA preferences for the Modeler section, you can set

the option to always open calculation views in performance analysis mode. You

can also set a threshold for the number of records in a table where performance

analysis mode will display a warning symbol next to this table. This will remind

you to pay special attention to very large tables. You can use partitioning on such

tables to improve performance, and you can apply filters that process only the

required data.

In addition to the cardinality, join type, and number of records in a table, perfor-

mance analysis mode will also provide you with warnings when the following is

true:

� Joins are based on calculated columns.

� Restricted columns are based on calculated columns.

Performance analysis mode works on the runtime artifact. So, even if you did not

build your calculation view yet, this mode will still give you valuable information.


Debug Mode

Right next to the Performance Analysis button on the toolbar is the Debug This

View button, as shown in Figure 12.2.

Figure 12.2 Activating the Debug Query Mode in a Calculation View

In Figure 12.3, we selected the Join node in the view, and opened the Debug Query

tab of this node that appeared on the top-right side of the editor.

You can see the SQL generated by the calculation view up to this point, as well as

the result set. This is very useful to debug your views, for example, if a field is miss-

ing or giving different results than what you expected. You can now trace it node

by node to see where you went wrong.

In Figure 12.4, we selected the next Join node. Comparing the SQL and the result set

with that of Figure 12.3, you can see that the new table that was joined in this node

added the PRODUCT_DESCRIPTION field.

Figure 12.3 Analyzing the SQL for a Join Node, along with the Result Set from That Node


Figure 12.4 Analyzing the SQL for the Next Node Join

The debug query mode works in the runtime view of the calculation view but uses

the active version of the calculation view. If you’ve made changes to your calcula-

tion view, you must Save and Build before you run debug query mode, or you

won’t get the correct results.

SQL Analyzer

The previous two tools are used while you’re designing your graphical calculation

views. The SQL Analyzer tool is used to measure the performance of a calculation

view when you run it.

Measuring performance of a running view clearly states that we’ll work with run-

time objects. For this, we have to open the SAP HANA database explorer in SAP

Web IDE for SAP HANA.

Select the SAP HANA Deployment Infrastructure (HDI) container and the Column

Views folder. A list of views that were successfully built is shown on the bottom-

left area. In Figure 12.5, we again selected the same Product view from the SHINE

content that we looked at earlier.

Right-click on Column Views, and select Generate SELECT Statement from the con-

text menu.


Figure 12.5 Generate SELECT Statement of a Column View in the SAP HANA Database Explorer

The SAP HANA database explorer tools will open the SQL Console and create a

SELECT statement. We now have a few tools at our disposal.

Tip

The SQL Console is a central tool for developers, and with new releases of SAP HANA, the

tools here will continue to be enhanced. These tools can be used to develop queries, trou-

bleshoot problems, and carry out ad hoc tasks.

SQL Console tabs persist across sessions, so you can log out and still keep the work you’re

busy with in these tabs for when you log in again.

Click on the tiny triangle next to the large green arrow to display the Run menu, as

shown in Figure 12.6. Here you can select the Prepare Statement tool.

When you select the Analyze dropdown menu next to the Run menu, you can

select the Analyze SQL option.


Figure 12.6 Opening the Run Menu to Select the SQL Analyzer Tool

The SQL Analyzer tool opens in a separate tab. It’s divided into two halves, as

shown in Figure 12.7. The top part has the following three tabs:

� Overview

These tiles show the time taken to run the query, how many tables were used,

how many records were returned in the result set, and how much memory was

used.

� Plan Graph

This shows a graphical tree of how long each step in the query took and how

many records were returned.

� SQL

This just shows the same SQL query we saw in the SQL Console.

The bottom part has the following four tabs:

� Operators

This provides more detail on every operator used when executing the query.

� Timeline

This presents timelines for the various operations and steps of the query.

� Tables in Use and Table Accesses

This gives details on the tables used in the query.


In the Overview tab, shown in Figure 12.7, you can see how long it took to execute

the query, what operators were mainly used, how many tables are used, how many

records were returned, and how much memory was consumed.

Figure 12.7 SQL Analyzer Tool Displaying the Overview Tab

The Plan Graph visually shows how the SQL query will be prepared and executed.

Figure 12.8 illustrates the prepared execution plan. The blocks show the time taken

by this step in the query, and the lines going to the next block up show how many

records will be sent up to that step in the query.

This information is helpful when you want to reduce the data transferred between

layers as quickly as possible or filter as early as possible, as recommended. You can

view the plan and find out how many records will be generated in the result set.

Then, you can apply a filter, for example, and rerun the Plan Graph to see the differ-

ence.

Figure 12.9 shows the Operators tab. Here you can filter and sort each of the query

steps, see the operators used, and note which steps took the longest, accessed the

most database objects, and created the most output rows.


Figure 12.8 Plan Graph Tab Displayed in the Top Half of the SQL Analyzer Tool

Figure 12.9 Operators Tab Displayed in the Bottom Half of the SQL Analyzer Tool


Figure 12.10 shows a Timeline for each of the steps in the SQL query. Often a graph-

ical display of the time used in the query helps us to understand where to focus

our attention.

Figure 12.10 Timeline Tab Displayed in the Bottom Half of the SQL Analyzer Tool

Lastly, you can use the Tables in Use and Table Accesses tabs to see which tables are

partitioned, where each table is located, how many entries were processed in each

table, how many times each table was accessed, and how much time was spent pro-

cessing each table (Figure 12.11).

Figure 12.11 Table Accesses Tab Displayed in the Bottom Half of the SQL Analyzer Tool


SPS 04

The SQL Analyzer tool has more tabs in the bottom half in SAP HANA 2.0 SPS 04. There is a

new Recommendations tab, which does what is says. There is also a Compilation Sum-mary tab that provides information on the compilation time of queries.

You can also export and import SQL plans, and both analysis flow and scale-out analysis

have improved.

Tip

In Figure 12.6 you can also see a Report Code Coverage tool under the SQL Analyzeroption in the menu. When you do a call to a procedure with specific parameter values,

this tool will show you what code in your procedure was and wasn’t used. For example, in

an IF – THEN – ELSE – END IF statement, the code in the ELSE might never be used, in which

case, you could remove the entire IF statement.

SAP HANA Cockpit

The last tool we’ll briefly mention in this chapter is the SAP HANA cockpit. This

tool is mainly used by SAP HANA system administrators, but it contains some

tools that can occasionally be used to help with performance optimization.

Figure 12.12 shows the relevant areas of the SAP HANA cockpit, namely the Moni-

toring area and the Alerting and Diagnostics area.

There are three links that are especially relevant to optimization:

� View trace and diagnostic files

This link shows a list of existing trace and log files. We can filter this list, for

example, to the “xs” search string, meaning that it only shows files that have

“xs” in the file name. These trace and log files can provide detailed process

information, and SAP HANA can trace very specific events; for example, you can

limit a trace file to a specific user or application.

� Plan trace

This link is used to set up and create specific traces and various trace configura-

tions. You’ll need to work with your SAP HANA administrator if you want to run

any traces, as this could severely affect the performance of the system.

� Troubleshoot unresponsive systems

This link allows you to identify long-running queries in the SAP HANA system.


Figure 12.12 SAP HANA Cockpit: Monitoring Area and Alerting and Diagnostics Area

SPS 04

A Recommendations Application is available from SAP HANA 2.0 SPS 04. This applica-

tion will help you to enhance SAP HANA system performance according to existing SAP

HANA operating conditions, instead of having to manually determine optimizations. It

provides general recommendations based on various aspects of the SAP HANA system,

such as data statistics, index usage, and rules for SQL optimization. It will also suggest

load units for tables, partitions, and columns according to how frequently they are

accessed.

Best Practices for Optimization

Performance analysis mode suggested some improvements we could make to our

information view related to specifying the cardinality, using a referential join, and

potentially partitioning our tables. In this last section, we’ll discuss some addi-

tional best practices for modeling in SAP HANA. Remember that we mentioned a


few best practices earlier in the chapter when we summarized concepts you’ve

learned previously in the book.

We can divide these best practices into two areas: those related to the graphical

view design and those related to SQL and SQLScript.

Best Practices for Calculation Views

The following are some additional best practices to optimize calculation views:

� Reduce data transfer between calculation views.

� Reduce the intermediate results between nodes inside a calculation view.

� Try to create calculations (e.g., in calculated columns) as late as possible in the

model.

� Implement filters at a table level. Don’t build filters on top of a calculated col-

umn, as this will do a lot of calculation before you filter the data. By filtering

first, you can eliminate a lot of calculations.

� The SAP HANA query optimizer tries to push filters down to the lower layers.

Sometimes you have a node that gets inputs from multiple nodes, for example,

the Intersect node in Chapter 6. The data flow splits before this node and has

parallel nodes. If you have a filter in the nodes above, the optimizer has to push

the filter down through these parallel nodes. Normally, it won’t do it as the

semantic correctness might be affected. If you know that the semantic meaning

won’t be affected, you can set the Ignore Multiple Outputs for Filter flag to force

the filter pushdown.

� Don’t send all information to the analytics and reporting tools; instead, make

your reports more interactive, using actions such as “click to drill down,” and

make multiple smaller requests from SAP HANA. The SAP HANA system will use

less memory, be more efficient, and respond faster. With less data moving

across the network, the user experience improves. In addition, by sending less

information to the analytics and reporting tools, you make these tools more

mobile-friendly.

� Select only the fields you require; columnar databases don’t like working with

all the columns.

� Specify the cardinality in a join. This can improve the runtimes for large models

as the optimizer can prune an entire data source from a join if certain prerequi-

sites are met, like only reading data from the left-side table in a referential join.

Just make sure that you also update the cardinality setting if you update the

model at a later stage.


� For joins:

– Use left outer joins rather than right outer joins. Try to use n:1 or 1:1 cardinal-

ity with the left outer joins.

– Try to reduce the number of join fields.

– Avoid joins on calculated columns.

– Avoid joins where the data types of the join fields are different, as this will

need type conversions.

� Investigate whether partitioning your tables is a valid option.

� Let SAP HANA do the hard work such as aggregations and calculations.

� Identify recurring patterns, and put them into a reuse “library.” For example,

you might always join the order header and the order details tables. Put this

into a separate calculation view, and reuse it as a data source for multiple other

calculation views. This also helps with breaking monolithic calculation views

into smaller parts. Added advantages are that the smaller parts are easier to

understand, and the reduced duplication leads to less maintenance.

� Avoid mixing core data services (CDS) views with calculation views. Even

though you can use CDS views as data sources in calculation views, the perfor-

mance isn’t as good as when you use the tables directly in the calculation view

and create the view functionality yourself.

� Enable caching for calculation views where applicable. If the query results are

extremely fast to generate, it might not make sense to enable caching. Caching

can be activated and controlled by the system administrator at the database

level. You can control caching at the level of an individual calculation view in

the Advanced tab of the Semantic node. You can set the Cache Invalidation

Period so that it expires after a transaction changes data in one of the data

sources, after an hour or after a day. This choice depends on the data require-

ments of the individual calculation view.

� Use Star Join nodes for OLAP queries. You can get similar results with Join

nodes, but it won’t perform as well.

� Try to use dedicated tables or views for value help lookups. It will be faster, and

users will get consistent values across all calculation views that use this Value

Help dialog. You can filter the list of values for users through analytic privileges.

This will show them only the values they are allowed to see and use. We’ll dis-

cuss analytic privileges in the next chapter.


Tip

Remember that SAP HANA uses authorizations as a filter. If a user is only allowed to see

one cost center, that is essentially a “filter,” and SAP HANA will use it to speed up the

query. In this case, security isn’t an overhead as in many other systems but an optimiza-

tion technique.

Partitioning

We’ve seen partitioning mentioned a few times as a solution to some performance

issues. Partitioning was already introduced in Chapter 3, but let’s discuss it a bit

more.

Although partitioning is normally managed by the system administrator or data-

base administrator, it’s still good to understand how to use it. Many times, model-

ers are called on to help with partitioning as they understand the tables and their

use.

Partitioning is when we subdivide a column store table into multiple separate

“blocks.” For example, we can split the table into blocks where partition 1 has all

the records where people’s names start with the letter A, partition 2 for B, and so

on. These partitions can then be spread across multiple servers. We can also keep

the partitions on a single server. This can still improve performance as it’s now

easy for the database to select only the records for people with names that start

with the letter M, for example.

Tip

Partitioning can only be used with column tables—not with row tables.

Partitioning is different from table distribution where several (whole) tables are

distributed across multiple servers. In that case, the tables stay as a whole. Table A

gets loaded on server 1, table B on server 2, and so on. In that case, tables aren’t split

up, but distributed, whereas with partitioning, a single table gets split up.

Following are some of the reasons for partitioning tables:

� The load is balanced across multiple servers. Query performance improves as

multiple servers are all working together in parallel to help process the data of a

single table.


� If a query, for example, only asks for people’s names that start with the letter in

the example above, the request is only executed on the server where the parti-

tion resides.

� If a table is partitioned over multiple servers, the delta merge management is

more efficient.

SAP HANA will split tables with more than 2 billion records to improve perfor-

mance.

Partitioning is transparent to the SQL used for the queries. SAP HANA will auto-

matically handle the partitioning logic in the background. You don’t need to spec-

ify partitions in your SQL queries.

Partitions are defined when a table is created, or they can be created later with an

ALTER TABLE statement. You can specify this using either SQL or CDS. You have full

control over partitions; for example, you can split them, merge them, move them

to other servers, and so on.

SAP HANA supports three types of table partitioning:

� Round robin

This is the easiest type of table partitioning. A new record is automatically

assigned to the next partition in a circular fashion. You don’t have to under-

stand the data or specify which columns to use for the partitioning.

� Hash

For hash partitioning, you specify the columns to use for the partitioning. SAP

HANA computes hash values and distributes the data evenly. You don’t really

need to understand the actual content in the table.

� Range

Range partitioning requires that you know the data in the table really well. You

have to specify not only the columns to use for the partitioning but also in what

ranges to split the data, for example, by years 2010–2015, 2016–2018, and 2019–

2020.

We can even create multilevel partitions. This is when you partition the partitions.

For example, you can use hash partitioning to enable load balancing across multi-

ple servers and then use range partitioning at the next level to split the data per

year, perhaps for performance reasons.


SQL and SQLScript Guidelines

We mentioned several best practices for optimizing SQL and SQLScript in Chapter

8, including the following:

� Don’t use SELECT * statements.

� Try to avoid imperative logic with a loop or branch logic. SQL uses a set-oriented

approach for best performance. When you use imperative statements, you dic-

tate the logic flow, making it very hard for the SQL optimizer to produce a better

plan and parallelize the logic for improved performance.

� Avoid using cursors. Cursors are a powerful feature in most databases that allow

you to apply some action on one record at a time in a result set. The resultant

reduction in performance is similar to the imperative logic in the previous

point.

� Break large, complex SQL statements into smaller independent steps using SQL-

Script variables. This can improve the parallelization of the execution of the

query. The calculated result set can be referred to multiple times, eliminating

repetitions of the same SQL statements. It will also reduce the complexity,

decreasing your development time.

� Use procedures or grouping sets to return multiple results sets.

� Try to write code that is free from side effects. When code changes the state of

the database, for example, by creating or altering tables, inserting or updating

data, or deleting records, you slow things down. Read-only code, such as table

functions, doesn’t alter the state of the database.

� Use SQL syntax instead of the older calculation engine functions. SAP HANA has

to convert these to SQL—this process is called unfolding—and the SQL opti-

mizer might not be able to fully optimize this converted code.

Even though we might not use calculation engine functions in our SQLScript code

any longer, there are other places these can still appear. Figure 12.13 shows the

Expression Editor screen for a calculated column in a graphical calculation view. In

this case, we needed the IF() function, which isn’t available in an SQL expression.

It’s recommended that you use SQL in the Expression Editor screen whenever pos-

sible.

Tip

For more SAP HANA optimization and performance information, you can attend the

HA215 training course. You can find more information about this course at http://s-

prs.co/v487623.




Figure 12.13 Column Engine Expression Used in a Calculated Column


In this chapter, we covered the following important topics:

� Architecture and performance

We started this chapter by reviewing the architecture design principles of SAP

HANA and how everything contributes to optimal system performance. We also

summarized some of the best practices you’ve already learned in your informa-

tion-modeling journey.

� Performance analysis mode

Performance analysis mode provides suggestions while we’re designing a view

to potentially improve performance. This tool looks, for example, at join types,

referential integrity, cardinality, and partitioning.


� Debug query mode

Debug query mode shows the SQL generated in a calculation view up to the

selected node. This can be used to see where fields get added and how that

affects the result set.

� SQL Analyzer

The SQL Analyzer tool shows graphical flows for the SQL plans. We can see the

number of records returned per operator in the plan, the number of records,

and the time each step of the query took in the executed plan. We can also com-

bine the graphical flow with a timeline and an operator list.

� SAP HANA cockpit

The SAP HANA cockpit provides access to the SQL cache of all SQL statements

previously run in the SAP HANA system. We can also create and read trace files

for identifying exactly what happens in the SAP HANA system when a query is

executed.

� Best practices

Finally, we looked at some best practices for ensuring that SAP HANA informa-

tion models perform optimally.

Practice Questions









1. What combination enabled real-time computing?

� A. In-memory technology with solid-state disks

� B. In-memory technology and multicore CPUs

� C. Multicore CPUs and solid-state disks

� D. Multicore CPUs with large level 1 caches


2. What are the advantages of columnar storage? (There are 2 correct answers.)

� A. Improved compression

� B. Improved data reading performance

� C. Improved data writing performance

� D. Improved performance when all fields are selected

Tip

The following five items list almost exactly the same question, but the answers are differ-

ent each time.

3. What performance technique should you implement to improve join perfor-

mance?

� A. Use joins instead of unions for combining large data sets.

� B. Join on key fields between tables in a dimension view.

� C. Do NOT use dynamic joins if you require optimal performance.

� D. Always use referential joins in star join views.

4. What performance techniques should you implement to improve join perfor-

mance? (There are 3 correct answers.)

� A. Use unions instead of joins for combining large data sets.

� B. Always specify the cardinality of a join.

� C. Join as many tables as possible in a star join view’s data foundation.

� D. Use left outer joins instead of right outer joins.

� E. Always mark joins as dynamic to improve performance.

5. What performance technique should you implement to improve the perfor-

mance of SAP HANA information views?

� A. Build single large views.

� B. Implement union pruning conditions.

� C. Output as many fields as possible.

� D. Supply reporting tools with all the data in one transfer.


6. What performance technique should you implement to improve the perfor-

mance of SAP HANA information views that include SQLScript?

� A. Use imperative logic.

� B. Use SELECT *.

� C. Break large statements into smaller steps.

� D. Use dynamic SQL.

7. What performance techniques should you implement to improve the perfor-

mance of SAP HANA information views? (There are 3 correct answers.)

� A. Perform calculations before aggregation in your analytic views.

� B. Minimize the transfer of data between the execution engines.

� C. Investigate partitioning of large tables.

� D. Apply filters as late as possible.

� E. Push down aggregations to SAP HANA.

8. What information can you find in the plan graph? (There are 2 correct

answers.)

� A. Execution engines used

� B. Number of records returned in each step

� C. Time taken for each step

� D. Plan trace

9. What information can you find in the SAP HANA cockpit? (There are 2 correct

answers.)

� A. SQL generated in each step

� B. Trace and log files

� C. List of operators

� D. Plan trace


10. What information can you find in performance analysis mode when you view

the analysis for a Join node? (There are 2 correct answers.)

� A. Suggested filter for the tables in the Join node

� B. Total number of records returned by the node

� C. Time taken by the join

� D. Tables used by the join in the node



The combination of in-memory technology and multicore CPUs enabled real-

time computing.

Solid-state disks are still disks and relatively slow compared to memory.

Multicore CPUs with large level 1 caches help, but also need in-memory tech-

nology because level 1 caches are quite small, for example, 256 KB.


The advantages of columnar storage include improved compression and data-

reading performance. (Because data is compressed, you can read more data at

the same I/O speed.)

Improved data-writing performance is for row storage.

Neither column nor row has improved performance when all fields are selected

via SELECT *.


Joining on key fields between tables in a dimension view will improve join per-

formance.

The answer “Use joins instead of unions for combining large data sets” has joins

and unions swapped around: you should use unions rather than joins.

Dynamic joins are created to improve performance.

The word always makes the “Always use referential joins” answer wrong.


The following are the correct performance techniques to improve join perfor-

mance: use unions instead of joins for combining large data sets, always specify

the cardinality of a join, and use left outer joins instead of right outer joins.

You should try to use only one table in a star join view’s data foundation.


The word always makes the “Always mark joins as dynamic” answer wrong.


Implementing union pruning conditions will improve the performance of SAP

HANA information views.

You shouldn’t build a single large view; instead, build views up in layers. Use

SAP HANA Live as an example.

“Output as many fields as possible” is another way of saying “Use SELECT *”;

don’t do it. Instead, supply reporting tools with only the data they need right

now, make them interactive, and rather use multiple small requests.


Breaking large statements into smaller steps will improve the performance of

SAP HANA information views that include SQLScript.

Do NOT use imperative logic, SELECT *, or dynamic SQL if you need optimal per-

formance.


The following are the performance techniques you can implement to improve

the performance of SAP HANA information views: minimize the transfer of

data between execution engines, investigate partitioning of large tables, and

push down aggregations to SAP HANA.

“Calculation before aggregation” is reversed; to be correct, it should say “aggre-

gation before calculation.”

Filters should be applied as early as possible.


The plan graph shows the number of records returned and the time taken in

each step.

We don’t focus on the execution engines used.

The SAP HANA cockpit shows the Plan Trace.


The SAP HANA cockpit shows the Plan Trace feature and the trace and log files.

The debug query tool shows the SQL statements generated by the nodes in a

calculation view.

The SQL Analyzer tool show the operators used.


Performance analysis mode shows the total number of records returned and

the tables used.


Takeaway

You now have a checklist of modeling practices to ensure optimal performance for

SAP HANA information models, including guidelines for creating optimal SQL and

SQLScript.

You can use the SAP HANA cockpit to identify long-running and expensive state-

ments and to perform traces of specific queries and user activities. Performance

analysis mode helps improve your SAP HANA information models by providing

optimization suggestions.

Summary

You’ve seen that SAP HANA has been built from the ground up with many perfor-

mance enhancements to enable high-performing processes. However, you still

have to do your part if you expect the best results.

You’ve now created SAP HANA information models with graphical views and with

SQL and SQLScript. You’ve also learned how to optimize your information models.

In the next chapter, we’ll look at how to apply security to your finished SAP HANA

information models.

Chapter 13

Security


� Know when to use the different SAP HANA security features

� Understand how users, roles, and privileges work together

� Know how to create new users and roles

� Learn the various types of privileges

� Explain analytic privileges

� Assign roles to users

� Be familiar with the SAP HANA Deployment Infrastructure (HDI)

and SAP HANA extended application services, advanced model

(SAP HANA XSA) security concepts

Chapter 13 Security550

In this chapter, we’ll discuss how to implement security on the information mod-

els you’ve built. Security is frequently a specialist area, with people dedicated to

looking after system security and user administration.

We won’t go into all the details of SAP HANA security in this chapter, but we’ll give

you a good overview to help you with most of the scenarios you’ll encounter in

your day-to-day information-modeling work and those that are covered on the

exam.

Real-World Scenario

You get an error message in SAP HANA indicating that you don’t have suffi-

cient privileges to do a certain task. In cases like these, you’ll need to know

how to resolve this kind of error message. For this situation, the answer is

simply granting a right to a special user. In some cases, you may need to build

special roles and privileges.

In other cases, you may be asked by the project manager to assist with setting

up the security on the information models. Because you built these informa-

tion models in SAP HANA, you’ll have a good idea of which users should have

certain privileges.

You might also have to comply with certain regulations to ensure data secu-

rity and privacy by using data masking and data anonymization techniques.



knowledge of SAP HANA security.


topics:

� When to use SAP HANA security, and when to use application security

� How users, roles, and privileges work together

� Understanding system users and how to create new users

� Working with analytic privileges

� Assigning the various types of privileges to a role

� Building a new role, and assigning it to a user


� Understanding HDI and SAP HANA XSA security concepts

� Using masking and anonymization for data security and privacy

Note



Security determines what people are allowed to do and see in business applica-

tions. Although we want to stop any unauthorized people from getting access to

company information, we also want to allow business users to do their work with-

out hindrance. There are various ways to achieve this balance in SAP HANA.

In this section, we’ll discuss the basic use cases for security in SAP HANA and

important security concepts. We’ll then look at how to create users, how these

users can access the system, and what roles and privileges these users should get

to achieve their goals. We’ll also discuss data security in general and data masking

in particular.

Usage and Concepts

Let’s begin by looking at when to use security measures in SAP HANA before tack-

ling important concepts.

Use Cases

The use cases for implementing security in SAP HANA depend on the deployment

scenario. Let’s look at the different deployment scenarios and their security man-

agement:


In a deployment scenario in which SAP HANA is used as the database for busi-

ness applications such as SAP ERP, SAP Customer Relationship Management

(SAP CRM), and SAP Business Warehouse (SAP BW), you don’t need to create SAP

HANA users and roles for the business users. These users log in to the various

SAP systems via the SAP application servers, and all end-user security is man-

aged by these applications. This is shown on the left side of Figure 13.1. You’ll still

need to create a few SAP HANA users and roles for the system administrator

users, but you don’t need to create anything for the business users.


Figure 13.1 Security for SAP HANA as Database vs. Platform

� SAP HANA as a platform

In deployment scenarios in which SAP HANA is used as a platform, end users

need to log in and work directly in the SAP HANA system. In such cases, you

might need to create users and assign roles for these users. In this chapter, we’ll

focus primarily on these scenarios.

� SAP HANA Live

For SAP HANA Live, which we’ll discuss in Chapter 14, you also need end users to

log in and work in the SAP HANA system. However, sometimes they also work

in SAP HANA Live models via SAP applications. In this case, you need to manage

the users in the SAP applications and use the Authorization Assistant tool to

automatically create the correct SAP HANA Live users and roles inside the SAP

HANA system. This chapter won’t focus on this use case.

We’ve mentioned users and their roles in this section. In the next section, we’ll

look at how these concepts work together in SAP HANA.

SAP HANA

SAP HANA

Database

Non-SAP

SAP ERP

SAP CRMEnterprise data

warehouse(SAP BW)

SAP HANA SAP HANA

Browser and mobile

SAP HANAXSA

SAP HANA as a Database SAP HANA as a Platform


Security Concepts

Figure 13.2 illustrates how privileges, roles, and users fit together in SAP HANA:

� Privileges

Privileges are the individual rights you can grant to a role. These will finally

allow a user to execute a specific action in the system.

� Roles

A role in SAP HANA is a collection of granted privileges. You can combine sev-

eral roles into another role, which is called a composite role.

� Users

Users are identities in the SAP HANA system that you assign to business people,

modelers, developers, or administrators using your system. You don’t assign

privileges directly to users. The best practice is to always assign privileges to a

role and then assign that role to users.

Figure 13.2 Assign Privileges to Roles and Roles to Users

Composite role

Role 1 Role 2

Privileges

System Analytic Application Object

User


We start the security process shown in Figure 13.2 from the bottom up. We start

with privileges, assign certain privileges to a role, and then assign that role to the

users. The privileges and roles all add up to give a complete portfolio of what users

are allowed to do. If something isn’t specified in a granted role or privilege, SAP

HANA denies access to that action.

Note

In SAP HANA, the default security behavior is to deny access. You can’t do anything in the

system until you’ve been granted a privilege.

In security, it’s also important to know the difference between authentication and

authorization:

� Authentication

Authentication answers the “Who are you?” question. It’s concerned with iden-

tifying users when they log in and making sure they really are who they claim to

be. In SAP HANA, this is set up when we create a user.

� Authorization

Authorization answers the “What are you allowed to do?” question. This identi-

fies which tasks a user can perform in the system. In SAP HANA, this is set up

when we create a role and assign that role to a user.

Now that we have the basic concept of roles and users, let’s take an in-depth look

at how security has changed with SAP HANA XSA and HDI.

SAP HANA XSA and SAP HANA Deployment Infrastructure

If you’ve worked with earlier versions of SAP HANA, you might get some unex-

pected surprises when you try do things the way you’ve always done them. In the

following sections, we’ll provide you with an understanding of the SAP HANA XSA

and HDI security concepts. In many places, we’ll focus on how these security con-

cepts differ from previous versions.

Application and Database

We already discovered in Chapter 4 that SAP HANA XSA can either run on the same

hardware as the SAP HANA database or can be deployed on separate hardware.

One of the consequences of this is that the SAP HANA XSA application has to be


separate from the database, which then leads to a separation of business (applica-

tion) users and technical (database) users, such as modelers, developers, and

administrators.

Business users aren’t stored in the SAP HANA system but are managed from the

Identity Provider (IdP) system. Users are created and managed, roles are assigned,

passwords updated, and users deactivated from the IdP system. The User Account

and Authorization (UAA) component in SAP HANA XSA communicates with the

IdP system, as illustrated in Figure 13.3.

Figure 13.3 An Overview of the SAP HANA XSA Security Concepts

Modelers, developers, administrators, and database object owners are, however,

still managed from the SAP HANA database. Just like there is a separation of appli-

cation users and database users, we also have a separation of application users and

database privileges.

SAP HANA DatabaseSAP HANA Deployment Infrastructure (HDI) – Contains run-time objects

C1 (HDI Container)

Classical DB schema C2 (HDI Container)

T1 (Table) T2 (Table)S2 (Synonym)S1 (Synonym)

C1#DI

T3 (Table)

with user-provided service with HDI service

SAPHANAXSA

Multi TargetApplication(MTA)

Application ContainerUser Account

and Auth (UAA)

Identity Provider (IdP )

(use SAML)Business users

Application privileges (scopes)



Technical users

Role C2 ::access(Granted to C1#00)

User C1#00(object owner)

Design-time objects(“schema-free”)

Deployedintoschemas


Design-Time and Runtime Objects

The next big change is that design-time objects are now separate from runtime

objects. We used to store the design-time objects in the SAP HANA system where

we finally used it. If you used the same object in a development system, a test sys-

tem, and a production system, you would store three separate copies of the same

object. These local copies of the objects were then “activated” to become runtime

objects in the same SAP HANA system. These activated (runtime) objects belonged

to the _SYS_REPO user.

We now store a single copy of the design-time object in a Git repository and

“deploy” it to multiple systems. Modelers and developers create such design-time

objects, such as calculation views and procedures (refer to the top-right area of

Figure 13.3).

System administrators and database administrators (DBAs), on the other side,

manage the deployment process and the runtime objects in the containers of the

SAP HANA database.

The biggest difference for modelers and developers is that we now work in a

“schema-free” way. Because the same design-time object can be deployed to mul-

tiple systems, such as testing and production, we can’t explicitly reference data-

base schemas. Schema names can be different for different HDI containers. The

actual schema names are assigned as part of the deployment process.

By default, SAP HANA Studio always created roles as runtime objects in the local

SAP HANA system. These runtime roles can’t be transported from development to

testing and production systems.

In SAP Web IDE for SAP HANA and the SAP HANA cockpit, we now create design-

time roles that can be deployed to multiple target systems.

Containers Isolate Schemas

In SAP HANA 1.0, in general, we had several schemas in the Catalog area. Each

schema was basically just a collection of SAP HANA runtime objects. There was no

isolation of schemas, so it was easy to create a calculation view that joined tables

from two different schemas. We merely used explicit references to the tables.

If we now try to do the same in an HDI container, we get a You are not authorized

message. In the bottom half of Figure 13.3, you see an illustration of how we should

do this in HDI containers.


HDI containers contain similar runtime objects as in the classical schemas in the

Catalog area. These could be tables, indexes, calculation views, table functions, and

procedures. Each HDI container is equivalent to a database schema, but with some

important differences and extras.

The word “containers” implies that they are isolated from each other and from the

“outside world.” This is the main difference to keep in mind as we discuss HDI con-

tainers. The “extras” we mentioned are as follows:

� Each HDI container has its own technical user, that is, the owner of all the run-

time objects in the container (schema). For a container named C1, this user will

be called C1#00. This is different from before, where the _SYS_REPO user was the

owner of all runtime objects in the entire SAP HANA system.

� Each HDI container has another associated container that is used for application

programming interfaces (APIs) and storage. For a container named C1, this asso-

ciated container is called C1#DI.

� Each HDI container has a generic default role that is automatically created when

you create the container. For a container named C1, this role is called C1::access.

This role (shown much later in this chapter in Figure 13.10), contains a role for

running services that allows external users data access to this HDI container

(schema), as well as object privileges to read, insert, update, and delete data.

Let’s return to our example of creating a calculation view that joins tables from

two different schemas.

Referring to Figure 13.3, we do our modeling in an HDI container called C1. We cre-

ate our calculation view, called CV2, that joins table T1 from a classical schema with

table T2 in another HDI container C2. Because container C2 is isolated, we need per-

mission to read the table. We need to assign the C2::access role to the C1#00 user

and create a synonym S2 to point to table T2. Synonym S2 uses the built-in HDI ser-

vice to read table T2.

When we read table T1 in the classical schema, we also have to create synonym S1

to read the table. But because this table is in a classical schema, we can’t use the

HDI service, and we have to use a user-defined service. We also need the C2::access

role assigned to the C1#00 user.

If we access a table in the same HDI container, such as table T3, this works as usual.

No extra steps are required because this table is located in the same container.


Note

HDI containers only work with the “newer” types of SAP HANA modeling and develop-

ment objects. You can’t use attribute views, analytic views, scripted calculation views, or

XML analytic privileges.

Classical Schemas versus SAP HANA Deployment Infrastructure Containers

Table 13.1 gives a summary of the differences between the SAP HANA 1.0 classical

schemas and SAP HANA 2.0 HDI containers. We do generalize a bit with these cat-

egory descriptions because HDI containers were already available in SAP HANA

1.0, and you can still use classical schemas in SAP HANA 2.0. But the focus in SAP

HANA 1.0 was on classical schemas, and in SAP HANA 2.0, the focus is on HDI con-

tainers.

With that background, we can now start looking at how to create users and roles in

SAP HANA.

SAP HANA 1.0 Classical Schemas SAP HANA 2.0 HDI Containers

Schemas and explicit references Containers and synonyms

Schema-specific design Schema-free design

Attribute views, analytic views, scripted

calculation views

Calculation views, table functions

XML analytic privileges and SQL analytic

privileges

SQL analytic privileges

Application and database privileges

together in the SAP HANA system

Application privileges separate from data-

base privileges

Business users and technical users together

in the SAP HANA system

Business users via IdP, technical users in the

SAP HANA system

One technical user: _SYS_REPO Technical user per HDI container:

<CONTAINER-NAME>#00

Design-time objects stored in the

SAP HANA repository

Design-time objects in the Git repository

Activation Deployment

Runtime roles (SAP HANA Studio) Design-time roles

Table 13.1 Comparing Security on SAP HANA 1.0 Classical Schemas with Security on SAP HANA 2.0 HDI Containers


Users

In this section, we’ll start by looking at how to quickly create new developer users

on a new SAP HANA development or sandbox system. We’ll then show how to cre-

ate and manage other users.

Creating and Assigning SAP HANA XSA Development Users

Modelers, developers, and administrators are normally the first to get working on

a new system. You can quickly create new modeler and developer users for SAP

HANA XSA using the SAP HANA XSA Administration tool. You see the tool’s

launchpad menu in Figure 13.4. (We discussed this tool in Chapter 4.)

Simply click on the User Management menu option. You’ll see a list of existing SAP

HANA XSA users. Then click the New User button on the left side of the work area

to create a new SAP HANA XSA user.

Figure 13.4 SAP HANA XSA User Management

After you’ve created the modeling or development user, you can add a Role Collec-

tion for this user, as shown in Figure 13.5. This will assign the user the relevant priv-

ileges to do the functions described below each role name.


Figure 13.5 Assigning the Role Collection to an SAP HANA XSA User

The last step is to assign your newly created modeling user to a development

space. (We discussed spaces in Chapter 4.) In the SAP HANA XSA Administration

tool, select the Organization menu option, as shown in Figure 13.6. For SAP HANA,

express edition, the organization is HANAExpress, and the space you need to

choose in this case is development.

Click the Add Members button, and type the name of the new user. Select whether

the new user is a Developer, Manager, or Auditor of the space. For modeling, you

specify the Developer option. Then click the OK button.

It’s a best practice to create your own user instead of using the initial administra-

tion users, called SYSTEM and XSA_Admin. The SYSTEM user is the main SAP HANA user.

The XSA_Admin user is the main SAP HANA XSA user. When the system is installed,

the administration users are required to get things going. However, don’t be sur-

prised when you very quickly get authorization messages saying that the SYSTEM

user doesn’t have privileges to perform certain tasks. Remember that, by default,


SAP HANA denies access to everything. Any new SAP HANA containers or models

that you’ve created since the system installation are new objects. You have to

explicitly grant access to these new models to the SYSTEM user before the user is

allowed to use them.

Figure 13.6 Assigning the SAP HANA XSA User to a Space

Note

Auditors normally require that the SYSTEM user is locked and disabled in an SAP HANA sys-

tem to improve security.

SPS 04

SAP HANA has never had the ability to manage a group of users. When you assign roles, it

was always to individual users. In SAP HANA 2.0 SPS 04, we now have the ability to man-

age user groups. This new feature is available in the SAP HANA cockpit.


You can create a user group and add users to the group. You can also configure the user

group, for example, by setting a password policy for all users in the group. Every user

group can have its own dedicated administrators, and only those designated administra-

tors can manage the users in this group.

Creating and Managing Users

To begin using security in SAP HANA, open the SAP HANA cockpit, as discussed in

Chapter 4. Go to the User & Role Management area in the SAP HANA cockpit, as

shown in Figure 13.7. Here we can manage users for the HDI.

Figure 13.7 Security Areas in the SAP HANA Cockpit

In this area, you can start creating users and roles by selecting the Manage Users

link. In Figure 13.8, we see an existing modeling user. This is the user we created in

Figure 13.4 using the SAP HANA XSA Administration tool. We could also have cre-

ated the same user in the SAP HANA cockpit.

This user was created as a restricted user, meaning it will access the system via

HTTP. This is exactly what we do when we use SAP Web IDE for SAP HANA. To


create a new user, click the + button on the bottom left of the screen. You can

choose if the new user should be a normal user or a restricted user.

Figure 13.8 Existing Modeling User in SAP HANA

The New User or New Restricted User dialog will appear, as shown in Figure 13.9.

You can choose a User Name for the user and a validity period.

In the AUTHENTICATION area, you can specify if you can log in using a user name

and password, or whether you should use one of the many single sign-on (SSO)

options available in SAP HANA. The most popular SSO option is Kerberos, which

most companies use together with their Microsoft Active Directory services. When

a user logs in to the company’s network infrastructure, the Kerberos server issues

a security certificate. When that user gets to the SAP HANA system and the Ker-

beros option is enabled for that SAP HANA user, the system will automatically ask

for and validate this certificate and will then allow the user access to the SAP HANA

system. With SSO, users don’t have to enter a user name and password each time

they use the SAP HANA system.


Figure 13.9 Screen for Creating a New User


Tip

Users in an SAP HANA system aren’t transportable. You can’t create users in a develop-

ment system and transport them to the production system with all their roles and privi-

leges. This is by design, as you don’t want development and modeling users in the

production system, and you don’t want end users in the development system.

Roles

In the previous section, we looked at creating users and assigning them roles. In

this section, we’ll take a deeper dive into SAP HANA’s roles and discuss the various

template roles available and the steps for creating a role in the system.

Template Roles

SAP provides a few template roles that you can use as examples for building your

own, or you can use them as a starting point and extend them. We don’t recom-

mend modifying any of the template roles directly; instead, copy them and mod-

ify the copy.

The following two template roles are important:

� MONITORING

This is a role that provides read-only access to the SAP HANA system. You pro-

vide this, for example, to a system administrator or monitoring tool that needs

to check your system. Users that have this role assigned can’t modify anything

in the system.

� MODELING

This role is given to modelers so they can create information models in the SAP

HANA system. We don’t recommend giving this role to any user in the produc-

tion system. Users with this role assigned can create and modify objects in the

system and can read all data, even sensitive or confidential data.

These types of roles are also known as global roles. They are schema-independent.

Creating Roles in SAP HANA Cockpit

You can see all the roles in SAP HANA by clicking the Manage Roles link in the SAP

HANA cockpit, as shown earlier in Figure 13.7. A list of all the roles in SAP HANA, as

shown in Figure 13.10, will show in the left column. When you select a role, the

details appear on the right side.


Figure 13.10 List of Roles in SAP HANA, with the ::access Role Selected

To create a new role, click the + button on the bottom left of the screen. In Figure

13.10, we selected the ::access role for our SAP HANA Interactive Education

(SHINE) container. We discussed this role earlier. Here you can see the object priv-

ileges to read (SELECT), INSERT, UPDATE, and DELETE data. At the bottom, you find the

role assigned, and the Grantor is the container#00 user that owns all the runtime

objects in this container.

SPS 04

Finding out which authorizations to include in a role can take time, especially when

you’re trying to find out why a role doesn’t work and what authorization is missing. SAP

HANA 2.0 SPS 04 has new simplified authorization troubleshooting to help with finding

out what privileges are required when a user is denied access to a view.

When an Insufficient Privilege error is generated, the user will receive a GUID—something

like 7DO69BEEB327067947CA291F4CA3DFFF. You can use this GUID to retrieve information

about the missing privilege by using a system procedure, as follows:

call SYS.GET_INSUFFICIENT_PRIVILEGE_ERROR_DETAILS('7DO69BEEB327067947CA291F4CA3DFFF', ?)


SPS 04 also includes a new Authorization Dependency Viewer in the SAP HANA cockpit,

which also lets you visually inspect the privilege structure on a calculation view. This is a

much easier way to troubleshoot missing privileges.

Creating Roles in SAP Web IDE for SAP HANA

The last method of creating roles is by creating .hdbrole files in SAP Web IDE for

SAP HANA. These are design-time files and can thus be deployed to multiple target

systems.

SAP Web IDE for SAP HANA is still more developer-focused than modeler-friendly

for creating roles. It’s easier to create roles in the SAP HANA cockpit graphical edi-

tor than SAP Web IDE for SAP HANA code editor (shown in Figure 13.11) used for

creating and editing .hdbrole files.

Figure 13.11 Role Created in SAP Web IDE for SAP HANA


The example in Figure 13.11 is the admin.hdbrole role in the SHINE demo.

Tip

The .hdbrole files are design-time objects that can be transported to the production sys-

tem. This improves security because you don’t need someone with rights to create roles

in the production system.

Earlier, we mentioned that you should always create design-time files in a schema-

free manner. You can see that that the illustrated role doesn’t contain any schema

names. But what should you do if you really need to create a schema-specific

authorization where you have to specify the schema name?

In this case, you can create a single .hdbroleconfig file that contains the name of the

schema, as shown in Listing 13.1.

{"DemoRole":{

"SchemaReference1":{"schema":"DemoSchema1"

}}

}

Listing 13.1 A Sample .hdbcroleconfig File

You can now refer to this .hdbroleconfig file in many .hdbrole files, as shown in Lis-

ting 13.2.

"schema_roles":[{

"schema_reference":" SchemaReference1","names": ["Role1", "Role2"]

}]

Listing 13.2 .hdbrole File Referencing the .hdbroleconfig File

Instead of having to modify the schema name in many .hdbrole files, you only

have to update the single .hdbroleconfig file.

Tip

You should never create roles in the SQL Console using SQL statements. These roles are

immediately created as runtime objects and can’t be transported (deployed) to other sys-

tems.


Tip

If you want to give users with this role the right to assign the role to others users, you

need to include WITH GRANT OPTION privileges in the role. In this case, the name of the role

must end with a hash (#), for example, role#.

Privileges

In this section, we’ll discuss the different types of privileges available in SAP

HANA. We’ve already seen schema privileges in Figure 13.11. In Figure 13.10, shown

earlier, we can see the following tabs for five other types of privileges:

� System Privileges

� Object Privileges

� Analytic Privileges

� Application Privileges

� Package Privileges

When you build or extend a role, you can assign these different types of privileges.

Each of these privileges has its own tab in the Manage Roles editing screen. You can

add new privileges in each tab by clicking on the Edit button.

A dialog box will present a list of relevant privileges to choose from. All of these

dialog boxes have a text field in which you can enter some search text. The list of

available privileges will be limited according to the search text you type.

Tip

You can only add privileges. There is no concept in SAP HANA of taking away privileges

because the default behavior is to deny access.

System Privileges

System privileges can be associated with system administration. These privileges

grant users the rights to work with the SAP HANA system. Most of the system priv-

ileges are applicable to DBAs and system administrators. Figure 13.12 shows an

example of system privileges.


Figure 13.12 Assigning System Privileges to Roles

Object Privileges

We’ve seen object privileges used in the ::access role, as shown earlier in Figure

13.10. Object privileges are used to assign rights for users to data source objects,

including schemas, tables, virtual tables, procedures, and table functions.

Granting object privileges requires a very basic knowledge of SQL syntax. This

makes sense because you’re working with data sources. You’ll have to know basic

SELECT, INSERT, UPDATE, and other SQL statements. Figure 13.13 shows an example of

assigning object privileges.

Figure 13.13 Assigning Object Privileges to Roles


When you grant, for example, a SELECT object privilege, the user will be able to read

all the data in the table or view on which you granted that object privilege.

Analytic Privileges

With object privileges, you can grant rights for someone to read all the data from a

data source—but how do you limit access to sensitive and confidential data, or

limit access to the data that people can see or modify to certain areas only? For

example, you may need to ensure that managers can only edit data of employees

reporting directly to them, that someone responsible for only one cost center

accesses only the correct information, or that only board members can see finan-

cial results before they are announced.

Analytic privileges can be used to limit data at the row level. This means that you

can limit data, for example, to show only rows that contain data for 2019, the

employees in your team, or your cost center.

Tip

We’re sometimes asked how to limit data at the column level. For example, a company

wants to allow employees to see all their colleagues’ details, except their salaries. How do

we hide the Salaries column?

This isn’t possible with analytic privileges, but it’s possible with views. Simply create a

view on the employees table, make sure the Salaries column isn’t part of the output field

list, and then grant everyone access to the view.

Figure 13.14 shows how to assign analytic privileges to a role. The figure shows only

a small portion of the work involved. Here, we assign the previously created ana-

lytic privileges to a role. We’ll look at how to create these analytic privileges later in

this section.

Figure 13.14 Assigning Analytic Privileges to Roles


Tip

In Figure 13.14, the role was assigned an analytic privilege called _SYS_BI_CP_ALL. Be care-

ful when assigning this analytic privilege; it grants access to the entire data set of the

information views involved. It effectively bypasses the analytic privilege restrictions

defined on a view. This analytic privilege shouldn’t be granted to end users in a produc-

tion system.

Note that this privilege is part of the MODELING role, as shown here. The MODELING role

shouldn’t be granted to any end users in a production system as this will allow them to

see all the data in the production system!

Think about how analytic privileges behave when they are used with layered infor-

mation views. Assume you need access to information about your cost center. Let’s

view this from the reporting side. You run a report that it calls view A. View A then

calls another view, called B. (View A is built on top of view B.) You can only see your

cost center information if you have an analytic privilege that allows you to see the

information being assigned to both views.

If you have the analytic privilege assigned to only one of the two views, you won’t

be able to see your cost center information.

Tip

It will help you to think of analytic privileges across multiple stacked views as an inner

join. Only the analytic privileges specified in both views are applied.

Another aspect of analytic privileges to think about is what result you want to

achieve when using multiple restrictions. Let’s assume you have two restrictions:

the first is year = 2018, and the second is cost center = 123.

� If you need year = 2018 AND cost center = 123, you must specify both restrictions

in a single analytic privilege. In this case, you’ve limited what the user sees even

more than when you use only one of the restrictions. The AND clause restricts the

results more.

� If you need year = 2018 OR cost center = 123, you must create two separate ana-

lytic privileges and assign both to the same view. In this case, you’ve increased

the amount of data in the result compared to just using one of the restrictions.

The OR clause lets the user see more.

Tip

Analytic privileges are the most-used privileges for information-modeling work.


You can create analytic privileges in the same context menu where you created all

your SAP HANA information views. In the context menu, select New • Analytic

Privilege. This creates an .hdbanalyticprivilege file. In the next step, choose the

information views that you want to restrict access to.

After you’ve selected the information views, you can create the row-level access

restrictions. On the left side of the screen in Figure 13.15, you can add more infor-

mation views. On the right side of the screen, create your restrictions.

Figure 13.15 Assigning Analytic Privilege Restrictions

There are three ways to specify the restrictions:

� Attributes

With this option, you first select the attributes that you want to create the

restrictions on. In this example, we chose the COMPANYNAME field. You then

have to specify the restrictions. In this example, we restrict the supplier to be

SAP only.

In Figure 13.15, we show Attributes restrictions, which is the default. You can also

restrict the validity period of the analytic privilege, as shown in Figure 13.16.


Figure 13.16 Specifying the Validity Period of an Analytic Privilege

� SQL Expression

If you used the running example with the Attribute option and then changed

the selection to SQL Expression, SAP HANA Studio automatically converts this to

the equivalent of an SQL WHERE clause—in this example:

(COMPANYNAME= 'SAP')

We can change the restriction type from the dropdown menu on the top right,

as shown in Figure 13.17. We merely changed the Attribute restrictions from

Figure 13.16 to an SQL Expression type restriction. The system automatically con-

verted it into the SQL WHERE clause shown. This included the validity period.

Figure 13.17 Changing the Restriction Type to an SQL Expression


� Dynamic

The last option is the most powerful. We can also use a procedure to create the

WHERE clause dynamically. This must be a read-only procedure that only has one

output parameter. The output it generates must be the same as the WHERE clause

shown earlier, for example, (COMPANYNAME= ‘SAP’).

In Figure 13.18, we select a procedure. You can also use a synonym that points to

a procedure in another HDI container or schema.

Figure 13.18 Selecting a Procedure for Generating Dynamic Analytic Privileges

As a modeler, you’ll sometimes get error messages. One of the most common

error messages when working with analytic privileges occurs when you build the

analytic privilege. You’ll get a Cannot use structured privilege for view not regis-

tered for STRUCTURED PRIVILEGE CHECK error message. The Build will fail as shown

in Figure 13.19.

In your analytic privilege, you specified the views that this analytic privilege

applies to. One of these views isn’t configured to use SQL analytic privileges.


To solve the error, open the view. In the Semantics node of the information view,

set the Apply Privileges property to SQL Analytic Privileges, as shown in Figure 13.19.

The analytic privilege should now build successfully.

Figure 13.19 Solving the Build Error for an Analytic Privilege

Application Privileges

Application privileges are used to grant rights to SAP HANA extended application

services, classic model (SAP HANA XS) applications. These application privileges

are created by the developers of these web applications. In this step, you merely

assign these privileges to a role.

As discussed at the start of this chapter, with SAP HANA XSA, most of this moves

from the SAP HANA database to the SAP HANA XSA applications.


Package Privileges

Package privileges were used to package design-time objects for deployment to

other servers, such as a production server. In SAP HANA 2.0 with SAP HANA XSA

and HDI, we use multi-target application (MTA) archives for deployment. Package

privileges are therefore not applicable in this case.

Assigning Roles to Users

Finally, we can assign our role with all its privileges to a user. To do so, go to the

User and Role Management area in the SAP HANA cockpit, as shown earlier in

Figure 13.7. Click the Assign role to users link.

You can look for a specific user as shown in Figure 13.20. To assign additional roles

to the selected user, click on the Edit button on the top left, and then click on the

Assign Roles button. After you’ve added the roles, click the Save button.

Figure 13.20 Assigning Roles to a User


Data Security

The topic of data security has always been important with SAP HANA because it’s

used as a database and a development platform. How and by whom the data is con-

sumed are inherent and expected features.

One of the basic principles of system security is segregation of duties. This means

that you’re not allowed to have only one person responsible for the administra-

tion of the security. Different people have different responsibilities, and the

responsibilities are divided in such a way that one single person can’t create and

assign rights to themselves or others that give them special privileges that can be

used for corruption or fraud.

As a modeler, you might be creating analytic privileges. Security administrators

then add these as part of a role, and the system administrators can assign this role

to people in the organization. The security auditors will check the system regularly

to ensure that the segregation of duties principle isn’t violated.

The system administrators set up the integration to the Lightweight Directory

Access Protocol (LDAP) server, such as Microsoft Active Directory, to enable SSO in

the SAP HANA system. They might also configure the Security Assertion Markup

Language (SAML) servers for enabling SSO in web browsers. This is required for

SAP HANA XSA to work securely in organizations.

Roles are only set up in a development server; they are assigned to users in the

production server. This segregates privileges across the landscape. Similarly,

developers and modelers can only write code and create information models in

the development server, but these are run by end users with the correct roles in

the production server.

SAP HANA has had data security features such as encryption of data files (data vol-

umes, log volumes, and backups) and audit logs for many years now. These are

configured by the system administrators and checked by the security auditors.

You can also encrypt individual table columns with SAP HANA client-controlled

keys.

Data masking is used when you want to reduce the exposure of sensitive informa-

tion. An example of data masking that you already know is when typing your pass-

word. Your password isn’t shown as text, but “masked” by displaying dots or

blanks.

Another typical use case is when working with credit card numbers. You have to

hide this information from DBAs and users with broad access. Even users that


work directly with the user data, such as call center agents, shouldn’t see the credit

card details.

Data masking can completely hide such information, or partially hide parts of it. A

call center agent might see just the last three digits of the credit card number to

verify with the customer that the correct card is used for a transaction.

Masking data in a column affects the display of the data. The data in the database

stays unchanged and can be still used in other information models and programs.

You can find the data masking feature in the Semantics node of a calculation view.

In the Columns tab, you’ll see a Data Masking column, as shown on the right side of

Figure 13.21. Here we created data masks for both the PHONENUMBER and

ACCOUNTNUMBER fields

Figure 13.21 Data Masking in the Semantics Node with a Data Masking Expression

Clicking in the Data Masking column field, a dialog screen opens where you can

add an Expression. You can create customizable mask expressions, and even

use the built-in functions of the expression editor. In Figure 13.21 the account


number—that could be a credit card number—is masked out, but it still shows

the last three digits.

Note

Data masking only works for columns of type VARCHAR and NVARCHAR.

Figure 13.22 shows what the masked data will look like to a user. Sensitive data in

both the PHONENUMBER and ACCOUNTNUMBER fields is masked.

Figure 13.22 Data Preview with Data Masking

Let’s say that a few special end users, such as the call center managers, need to see

the actual cleartext data? You could create another calculation view for these

users, but that would require double maintenance each time the view needs to be

updated. In this case, you grant the UNMASKED object privilege to the role

assigned to these users, as shown in Figure 13.23.

If you need to use SQLScript instead of graphical calculation views, you can use the

WITH MASK clause in your SQLScript statements, for example, when creating tables

or SQL views.

This completes our tour of the security aspects you need for working with infor-

mation models in SAP HANA XSA and HDI containers.


Figure 13.23 Granting the Unmasked Object Privilege


In this chapter, we covered the following terminology:

� Users

Identities in the SAP HANA system that you assign to business people using

your system.

� Roles

A collection of granted privileges in SAP HANA. You can combine several roles

into another role, which can be called a composite role.

� Template roles

Templates that you can use to build your own roles.

� Authentication

Identifies users when they log in and ensures they are who they claim to be.

� Authorization

Identifies which tasks a user can perform in the system.


� Privileges

The individual rights you can grant to a role or a user. In SAP HANA, there are

different types of privileges:

– System privileges: These privileges grant users the rights to work with the SAP

HANA system. Most of the system privileges are applicable to database and

system administrators.

– Object privileges: These privileges are used to assign rights for users to data

source objects, including schemas, tables, virtual tables, procedures, and

table functions. Requires very basic knowledge of SQL syntax.

– Analytic privileges: These privileges control access to SAP HANA data models.

There are two types of analytic privileges: classic XML-based analytic privi-

leges and the new SQL-based analytic privileges. There is a migration tool to

convert the old XML-based analytic privileges to the new SQL-based analytic

privileges.

� Restrictions in analytic privileges

Row-level access restrictions that can be implemented using analytic privileges.

Practice Questions

These practice questions will help you evaluate your understanding of the topic.

The questions shown are similar in nature to those found on the certification

examination. Although none of these questions will be found on the exam itself,

they will allow you to review your knowledge of the subject. Select the correct

answers, and then check the completeness of your answers in the “Practice Ques-

tion Answers and Explanation” section. Remember, on the exam, you must select

all correct answers and only correct answers to receive credit for the question.

1. Which are the recommended assignments in SAP HANA? (There are 2 correct

answers.)

� A. Privileges to users

� B. Roles to users

� C. Roles to roles

� D. Roles to privileges


2. In which SAP HANA deployment scenarios will end users be created as users

in the SAP HANA system? (There are 2 correct answers.)

� A. SAP BW powered by SAP HANA

� B. SAP S/4HANA

� C. SAP HANA as a platform

� D. SAP HANA Live

3. True or False: The default behavior of SAP HANA is to allow access.

� A. True

� B. False

4. Your newly created analytic privilege does NOT want to build. What must you

do?

� A. Grant rights to the _SYS_REPO user.

� B. Assign the ::access role to the HDI container user.

� C. Change the Apply Privileges property of the view.

� D. Change the analytic privilege to use an SQL Expression.

5. What do you have to assign to users to allow single sign-on (SSO)?

� A. SQL analytic privileges

� B. Kerberos authentication

� C. TRUST ADMIN system privilege

� D. Application privileges

6. True or False: Object privileges on a table with SELECT rights will allow you to

access all the data.

� A. True

� B. False


7. What can you use to limit users to see only 2018 data? (There are 2 correct

answers.)

� A. An object privilege

� B. A SQL analytic privilege

� C. A view with a filter

� D. A view with a restricted column

8. You have two calculation views for viewing manufactured products. The WORLD

calculation view shows worldwide information. The INDIA calculation view is

built on top of the WORLD calculation view and shows information for Indian

products. What privilege must you assign to see your department’s specific

data in a report built on the INDIA calculation view?

� A. An analytic privilege on the WORLD calculation view

� B. An analytic privilege on the INDIA calculation view

� C. An analytic privilege on both calculation views

� D. An object privilege on the WORLD calculation view

9. You want to read a table from an SAP HANA classical schema. What do you

need to do to get it working? (There are 3 correct answers.)

� A. Use an explicit reference to the schema.

� B. Make use of a user-provided service.

� C. Make use of the built-in HDI service.

� D. Create a synonym.

� E. Assign the provided ::access role to your container’s technical user.

10. True or false: SAP HANA XSA application users are managed from an external

Identity Provider (IdP) system.

� A. True

� B. False


11. Where do you assign a user as a developer in a development space?

� A. SAP HANA XSA Administration tool

� B. SAP HANA cockpit

� C. SAP Web IDE for SAP HANA

12. What do you use to ensure that each account holder of a bank account can

only see his or her own identity document details in a calculation view?

� A. Audit logs

� B. Analytic privileges

� C. Data masking



We can assign roles to other roles and roles to users in SAP HANA.

We could assign privileges to users in SAP HANA 1.0, but this wasn’t recom-

mended. In SAP HANA 2.0, this option was removed.

We can’t assign roles to privileges. We can only assign privileges to roles.


End users might be created as users in the SAP HANA system in the SAP HANA

as a platform deployment scenario. In the case of SAP HANA XSA, where the

application and database are separated, we also don’t need to create end users

in the SAP HANA database.

With SAP HANA Live, users are created in the SAP application server, and they

are automatically created by the Authorization Assistant in SAP HANA.

With SAP BW on SAP HANA and SAP S/4HANA, users are created in the SAP

application servers, not in SAP HANA.


False. The default behavior of SAP HANA is to deny access.

Note

True or False questions are included to help your understanding, but the actual certifica-

tion exam doesn’t have such questions.



If your newly created analytic privilege does NOT want to build, you must

change the Apply Privileges property of the view.

Granting rights to the _SYS_REPO user isn’t applicable to HDI containers because

each container has its own technical user.

Assigning the ::access role to the HDI container user is used for accessing data

from another container and is unrelated to this issue.

Changing the analytic privilege to use an SQL expression won’t solve the prob-

lem because all the types of restrictions in an analytic privilege are equivalent.

An SQL expression isn’t a super power!


You have to assign Kerberos authentication to users to allow SSO.


True. Yes, an object privilege on a table with SELECT rights will allow you to

access all the data.


You can use a view with a filter or a SQL analytic privilege to limit users to see

only 2018 data. A view with a restricted column will show all the data in all the

fields, except in the restricted column.


You need an analytic privilege on both calculation views to see the depart-

ment’s specific data in a report built on the INDIA calculation view.

If you only have access to one of the two views, SAP HANA will deny access to

the other view, and your reporting data will not show.


If you want to read a table from an SAP HANA classical schema, you need to

make use of a user-provided service, create a synonym, and assign the provided

::access role to your container’s technical user.

We work in a schema-free way with HDI containers, so you don’t use an explicit

reference to the schema.

The built-in HDI service is used when accessing a table from another HDI con-

tainer, not from a classical schema.


True. SAP HANA XSA application users are managed from an external IdP sys-

tem.



You can assign a user as a developer in a development space using the SAP

HANA XSA Administration tool via the Organization and Space Management

tile.

The SAP HANA cockpit is where you can create users and roles, as well as assign

roles to users.

You use SAP Web IDE for SAP HANA to create .hdbrole and .hdbanalyticprivilege

files.


You use analytic privileges to ensure that each account holder of a bank

account can only see his or her own identity document details in a calculation

view.

Data masking hides data from anyone who isn’t allowed to see all the data

Audit logs don’t control what data people can or can’t see. They just log what

data people have seen.

Takeaway

You should now have a good overview of security in SAP HANA, especially as it

relates to information modeling. You should understand when to use SAP HANA

security and how users, roles, and privileges work together.

You know how to create new users and roles and how to assign the various types

of privileges, and you can explain analytic privileges and how to create them.

In your journey, you also learned best practices and picked up practical tips to

work with in your SAP HANA projects.

Summary

We started by looking at when to use SAP HANA security and when to use an appli-

cation’s security. We then went into the details of creating users and roles and

assigning privileges. Along the way, we discussed analytic privileges, which are

particularly applicable to our modeling knowledge. We also looked at data security

and privacy, using data masking and data anonymization.


In our last chapter, we’ll explore how SAP HANA calculation views are used in SAP

ERP systems running on SAP HANA, and how this has evolved on the journey to

SAP S/4HANA.

Chapter 14

Virtual Data Models


� Understand the concepts behind SAP HANA Live

� Get to know the two architecture options for SAP HANA Live

� Learn about SAP HANA Live’s virtual data model (VDM) and

understand what you’re allowed to use and modify within it

� Choose the correct SAP HANA Live views for your reports

� Know how to use the SAP HANA Live Browser tool

� Know how to use the SAP HANA Live extension assistant

� Design your own SAP HANA Live views

� Extend and adapt standard SAP HANA Live views for your

reporting requirements

� Find out how the SAP HANA Live VDMs have evolved into the

SAP S/4HANA embedded analytics

Chapter 14 Virtual Data Models590

Thus far, we’ve primarily discussed using SAP HANA as a modeling and develop-

ment platform. In this chapter, we’ll turn our attention to the architecture deploy-

ment scenario, in which SAP HANA—via SAP HANA Live—is used as the database

for SAP business systems such as SAP ERP, SAP Customer Relationship Manage-

ment (SAP CRM), and SAP Supply Chain Management (SAP SCM). We’ll also look at

how SAP HANA Live has evolved over the past few years into SAP S/4HANA

embedded analytics.

Real-World Scenario

Imagine that you’re asked to help design and develop some new reports for

a company—but these aren’t the usual management reports. The company

wants to streamline its product deliveries.

The company has been struggling to continuously plan the shipping of its

products during the day. The company receives a shipping schedule from the

data warehouse system every morning, but traffic jams and new customer

orders quickly render this planned schedule useless. This causes backlogs at

the loading zones and costs the company more in shipping because it can’t

combine various customer orders in a single shipment.

Someone has tried using a Microsoft Excel spreadsheet, but the data isn’t

kept up to date, and it can’t consolidate the data from all the data sources.

After hearing about SAP HANA’s ability to analyze data in real time, the com-

pany asks you to create some real-time delivery planning reports.

You know that there are prebuilt SAP HANA Live information models avail-

able for the SAP ERP system, and you can use a few of these SAP HANA Live

information models to meet this business need quickly.

A few months later, the company migrated its SAP ERP system to SAP

S/4HANA. You know what is different in the new SAP S/4HANA embedded

analytics from the older SAP HANA Live, so you can guide the company to get

the solution up and running quickly using the new virtual data model (VDM).



The objective of this portion of the SAP HANA certification is to test your knowl-

edge of SAP HANA Live and SAP S/4HANA embedded analytics concepts. The certi-

fication exam expects you to have a good understanding of the following topics:

� SAP HANA Live concepts and architecture

� A high-level overview of SAP HANA Live implementation

� The architecture of SAP HANA Live with the VDM

� How to find and consume the right SAP HANA Live information models using

the SAP HANA Live Browser

� How to extend and modify the available SAP HANA Live models to address busi-

ness requirements

� A high-level view of SAP S/4HANA embedded analytics, and how this differs

from SAP HANA Live

Note

This portion contributes up to 5% of the total certification exam score. The exam ques-

tions for this portion include the topic of core data services (CDS), which we discussed in

Chapter 5.


Simply put, SAP HANA Live uses predelivered content in the form of calculation

views for real-time operational reporting. SAP S/4HANA uses predelivered content

in the form of ABAP CDS views. In this chapter, we’ll look first at the real-world sce-

nario again. Then we’ll walk through the history of business information systems

to understand the problem posed in this scenario better and determine how using

SAP HANA Live and SAP S/4HANA embedded analytics addresses this problem.

Then, we’ll look at what necessitated SAP HANA Live, similar to Chapter 3, in which

we discussed the impact SAP HANA’s fast in-memory architecture had on systems

that traditionally used slower disk memory.

From there, we’ll dive into the architecture of SAP HANA Live, its VDM, its many

views, and steps for installation and administration. After we’ve covered these

foundational topics, we’ll shift our attention to the primary tools used within SAP

HANA Live: SAP HANA Live Browser and SAP HANA Live extension assistant. Then

we’ll discuss the steps for modifying SAP HANA Live information models.


Finally, we’ll look at how the concepts of SAP HANA Live have evolved to SAP

S/4HANA embedded analytics using ABAP CDS views.

Reporting in the Transactional Systems

Many years ago, SAP only had a single product, called SAP R/3. This offering blos-

somed into the SAP ERP system that is still used today. SAP ERP did everything for

companies, from finances to manufacturing, procurement, and sales to delivery,

payroll, and store management. The SAP ERP system was a transactional system,

what we now call an online transactional processing (OLTP) system (also called an

operational system). All reports were run from within this system, although they

tended to be simple “listing” reports.

Over time, the need arose for more complex reports. In response, more advanced

analytics and dashboard tools were provided. Through these tools, people learned

how to use cubes to analyze data. When developers tried to build these new analyt-

ics into the transactional system, they discovered that these new reporting fea-

tures had a huge impact on the performance of the SAP ERP system. A single

reporting user running a large cube could consume more data from the database

than hundreds of transactional users. It was unacceptable that a single user could

slow the entire system down, causing hundreds of other users to suffer the conse-

quences of slow responses.

To address such performance issues, all reporting data from the transactional sys-

tem was copied to a newly invented reporting system. The era of data warehouses

had arrived. All reporting data is updated from the transactional system to the

data warehouse during the night. This way, the performance of the transactional

system remained acceptable during the workday. The new data warehouses were

called online analytical processing (OLAP) systems, and OLTP and OLAP went their

separate ways (see Figure 14.1).

This departure helped the performance of both systems but eventually led to

countless other headaches. Because data was only copied once every 24 hours,

reports were always outdated because yesterday’s data was used for the reports.

We couldn’t see what was happening in the OLTP system in real time. People then

started using Microsoft Excel spreadsheets to address some of the issues, which in

turn led to more problems and risks (e.g., reports that didn’t agree, wrong formu-

las, and an inability to consolidate information).


Figure 14.1 Separate OLTP and OLAP Systems

Note

Our real-world scenario at the start of the chapter provides a good example of how yes-

terday’s data and planning can’t address today’s traffic jams.

SAP HANA can solve such problems because it can work with both OLAP and OLTP

data in a single system, without causing slow performance when reporting users

analyze large data sets.

By introducing SAP HANA Live, which moves the operational reporting function-

ality from the data warehouse back into the operational system, SAP solved many

of the problems we just mentioned.

If you’re wondering if SAP HANA makes data warehouses such as SAP Business

Warehouse (SAP BW) irrelevant, the short answer is no because data warehouses

are multipurpose. In the real-world scenario we used for this chapter, we only

looked at the operational reporting aspects of data warehouses, which aren’t par-

ticularly strong points for such systems. If you had a single operational system

with a single warehouse for reporting, you could do everything with SAP HANA—

but reality tends to be more complex.

Most companies use their data warehouses to consolidate data from many sys-

tems, as shown in Figure 14.2. They use data warehouse features such as the follow-

ing:

� Data governance

� Preconfigured content in the form of cube definitions and reports

� Data lifecycle management, including data aging and a complete history of

business transactions

Data updated every night

Database(OLTP)

SAP ERP,SAP CRM,SAP SRM,

SAP GRC, …

Database(OLAP)

Datawarehouse


� Cross-system consistency and integration

� Planning, consolidation, and consumption

Figure 14.2 Data from Multiple Business Systems Consolidated into the Data Warehouse for Report-ing, Transactional History, and Aging

Tip

It makes no sense to try to get rid of data warehouses completely due to some opera-

tional reporting issues. A better solution is to focus on addressing the few problematic

areas in which data warehouses aren’t optimal.

Because SAP BW is primarily used for data warehousing, operational reporting

needed to be shifted back to SAP ERP. To enable this functionality, SAP ERP now

uses SAP HANA as a database. This allows companies to run their operational

reports, even with complex analytics, on real-time SAP ERP data directly from the

underlying SAP HANA database via calculation views.

Database(OLAP)

Database(OLTP)

SAP ERP DataWarehouse

SAP SRM

Microsoft

SAP CRMLegacy

SAP Ariba


Note

The SAP HANA Live calculation views were built using the SAP HANA Studio based on

tables in a classical database schema. They aren’t .hdbcalculationview files, and don’t use

data sources in SAP HANA Deployment Infrastructure (HDI) containers. Up to now, we’ve

focused on the SAP HANA extended application services, advanced model (SAP HANA

XSA) concepts, whereas SAP HANA Live was built using the older SAP HANA 1.0 approach.

SAP HANA Live

SAP HANA Live is the tool used to install, select, maintain, and modify the SAP

HANA calculation views used for operational reporting in the SAP ERP system. SAP

HANA Live isn’t limited to SAP ERP but also is available for many other SAP busi-

ness systems, such as SAP CRM, SAP SCM, SAP Governance, Risk, and Compliance

(SAP GRC), and SAP Product Lifecycle Management (SAP PLM).

Some of the benefits of SAP HANA Live include the following:

� Real-time reporting and analytics are run on up-to-date information.

� SAP HANA doesn’t need to store precalculated aggregates in cubes.

� OLTP and OLAP run in the same system, which can help reduce the total cost of

ownership (TCO).

� Access is provided for any reporting tool or application.

� Customers and partners can extend SAP HANA Live to suit their needs.

Now that you have a better understanding of how SAP HANA Live came to be, the

following subsections will dive further into SAP HANA Live’s architecture, VDMs,

views, administration procedures, and modifications.

SAP HANA Live Architecture

SAP HANA Live needs an SAP HANA database. Connecting to an SAP HANA data-

base can be achieved in two ways (in this case, we just concentrate on the SAP ERP

system, but this also applies to all the other SAP business systems such as SAP

CRM, SAP SCM, SAP GRC, and SAP PLM; see Figure 14.3):


The first option is to migrate an SAP ERP system’s database to SAP HANA. No

additional systems are required. This is also known as the integrated scenario.


� SAP HANA as a side-by-side accelerator

The second option is to keep your current SAP ERP database, for example,

Microsoft SQL Server, and install an SAP HANA system as a side-by-side acceler-

ator (as discussed in Chapter 3). Then, you replicate the required database tables

to the SAP HANA system using SAP Landscape Transformation Replication

Server. You can find more information about SAP Landscape Transformation

Replication Server in Chapter 9. SAP Landscape Transformation Replication

Server enables real-time replication so that SAP HANA always has the latest data

available. For this scenario, you need to install the SAP Landscape Transforma-

tion Replication Server and SAP HANA systems.

Figure 14.3 Architecture Options for SAP HANA Live

After you’ve connected to an SAP HANA database, you can build reports, analytics,

and dashboards directly on the SAP HANA Live content. Other applications can

also call this content directly.

Note

In both architectural scenarios, SAP HANA Live is installed in the SAP HANA database.

SAP ERP

Other Database

SAP HANAwith SAP HANA

Live loaded

SAP ERP

SAP HANA

with SAP HANALive loaded

Option 1: SAP HANA as a database

SLT

Option 2: SAP HANA as a sidecar

Reports & Analytics

Replication


SAP HANA Live Virtual Data Model

SAP HANA Live content consists primarily of calculation views that are designed,

created, and delivered by SAP. These calculation views are built for operational

reporting instead of analytical reporting purposes and, therefore, use mostly join

nodes. The information models are relational rather than dimensional (cube-like).

A system administrator loads the SAP HANA Live content into the SAP HANA data-

base. These calculation views are layered in a structure called the SAP HANA Live

virtual data model (VDM), as mentioned earlier. SAP HANA Live is structured inter-

nally as shown in Figure 14.4.

Figure 14.4 SAP HANA Live VDM

The VDM consists of the following types of views:

� Private views

These are built directly on the database tables and on other private views. These

denormalize the database source data for use in the reuse views.

Database Tables

SAP HANA Live – Virtual Data Model (VDM)

Reports, Analytics,and Applications

Private Views

Reuse Views

Query Views

Value Help

Views

Standard SAP HANA Live content

Customer Query Views

Customer Extensions


� Reuse views

These structure the data in a way that business processes can use. These views

can be reused by other views.

� Query views

These can be consumed directly by analytical tools and applications. They

aren’t reused by other views. These views are used when designing reports.

� Customer query views

These are created either by copying SAP-provided query views or by creating

your own query views. You can add your own filters, variables, or input param-

eters to these views. You can also expose additional data fields or hide fields

that aren’t required.

� Value help views

These are used as lookups (i.e., dropdown lists or filter lists) for selection lists in

analytical tools and business applications. They make it easier for users to filter

data by providing a list of possible values to choose from. Value help views

aren’t used in other VDM views; they are consumed directly by analytical tools

and business applications.

Tip

You should never call private and reuse views directly.

You can build your reports, analytics, or business applications directly on query

views. These can be either SAP-provided query views or custom query views. When

you run your report, these query views are called. They in turn call the relevant

reuse views, which call the relevant private views, which in turn read the relevant

database tables. The value help views are called whenever dropdown lists are used

to display possible values that a user can choose from.

Note

Modifying any SAP-provided VDM views isn’t recommended because all SAP-provided

VDM views are overwritten when a newer version of the SAP HANA Live content is

installed. There is no mechanism like the one in ABAP, in which the system asks if you

want to keep any customer changes.

Therefore, you should always make a copy of a VDM query view to modify, rather than

modifying the original.


SAP HANA Live Views

Most of the SAP-provided SAP HANA Live views are calculation views. All the SAP

HANA modeling techniques that you’ve learned are relevant to these views. Figure

14.5 shows an example of such a calculation view in the SAP HANA Studio.

Figure 14.5 SAP-Provided Calculation View (OverallBillingBlockStatus) in SAP HANA Live for SAP ERP

Tip

In SAP HANA 2.0, we don’t use the SAP HANA Studio. You see references to the SAP HANA

Studio here because, as we mentioned already, SAP HANA Live still uses the older SAP

HANA 1.0 modeling approach.

You’ll notice several characteristics of these calculation views in Figure 14.5:

� Several nodes in layers

This is to denormalize the source data. Normalizing data is normal in an OLTP

system to improve performance but isn’t required for analytics.

� Multiple joins

These are used to combine reuse views to create the VDM query view.

� Variables, input parameters, and calculated attributes

The use of input parameters has been limited due to the fact that some report-

ing tools have difficulty using them. Default values are always supplied for the

input parameters.


SAP HANA Live Administration

SAP HANA Live is installed in the SAP HANA database. A system administrator will

normally install the SAP HANA Live content package.

After SAP HANA Live is installed on an SAP HANA system, you’ll see the content in

the Content folder, under sap/hba, as shown in Figure 14.6. You can explore the cal-

culation views in the SAP HANA Studio. We don’t recommend that you change the

views here. As mentioned earlier, all the SAP-provided VDM views are overwritten

when a newer version of the SAP HANA Live content is installed. There is no mech-

anism in place within SAP HANA Live to ask if you want to keep any customer

changes, so you could lose all your hard work during an SAP HANA Live update.

Figure 14.6 SAP HANA Live Content Package in the SAP HANA Studio

Note

SAP HANA Live has its own release cycles and timelines. SAP HANA Live content updates

don’t have to correspond to the SAP HANA system update cycles and timelines.

In the sap/hba package, you’ll also find various web-based tools for working with

SAP HANA Live content:

� SAP HANA Live Browser

This tool is used to find the right query views for your reporting needs.

� SAP HANA Live extension assistant

This tool helps you copy and modify views based on your business require-

ments.

Let’s look at the full breadth of SAP HANA Live Brower’s functionality.

SAP HANA Live Browser

The SAP HANA Live Browser tool is a web-based (HTML5) application that enables

you to find the correct views for your reporting or analytics requirements. Some-

times, you might need to ask an SAP functional person that knows the source


system well to help you find the required views. For example, to find sales infor-

mation in an SAP ERP system, you might need the advice of an SAP ERP Sales and

Distribution (SAP ERP SD) consultant.

You can find the SAP HANA Live Browser tool (Figure 14.7) by accessing http://<SAP

HANA server hostname>:8000/sap/hana/hba/explorer. (This assumes that the

instance number is 00.)

In this section, we’ll walk through the different areas found in the SAP HANA Live

Browser, starting with the top menu’s ALL VIEWS section (see Figure 14.7).

Figure 14.7 SAP HANA Live Browser

Here you can search for views by typing a part of the name of the view in the Filter

field. For each view, you can see the following information:

� View name

� Description of the view

� View category (private, reuse, or query view)

� View type (mostly calculation views)


� To which area of the application the view is applicable

� In which SAP HANA package the view is located

Figure 14.8 shows a partial list of views that we can use for procurement reports.

Figure 14.8 Filter String Entry to Find Views

You can access many features in SAP HANA Live Browser from the provided tool-

bar (see Figure 14.9).

Figure 14.9 SAP HANA Live Browser Toolbar

The following functions are available from the toolbar:

� Open Definition

The Open Definition tab (Figure 14.10) shows the metadata of the SAP HANA Live

view. These fields are available for your reports and analytics. If you need other

fields, you’ll have to use the SAP HANA Live extension assistant.

� Open Content

The Open Content tab (Figure 14.11) shows a preview of the data an SAP HANA

Live view will produce.

� Open Cross Reference

The Open Cross Reference tab (Figure 14.12) shows an interactive and graphical

depiction of all the related tables and views used by this specific SAP HANA Live

view. You can also see an outline version instead of the graphical version.


Figure 14.10 Open Definition Tab Showing SAP HANA Live View Metadata

Figure 14.11 Open Content Tab Displaying Data Produced by an SAP HANA Live View


Figure 14.12 Open Cross Reference Tab Showing Related Tables and Views Used by This SAP HANA Live View (Graphical Version)

� Tag

The Tag tab (Figure 14.13) allows you to add your own keywords (tags) to describe

SAP HANA Live views. You can use these tags to group some SAP HANA Live

views that you would like to use in your reporting, analytics, or dashboards.

Figure 14.13 Adding Tags to an SAP HANA Live View

� Generate SLT File

The Generate SLT File icon allows you to generate configurations for SAP Land-

scape Transformation Replication Server to replicate only the tables that you

require for your reporting or analytics. This function is only required if you’re


using SAP HANA Live in a sidecar configuration. You won’t replicate the entire

source system to the SAP HANA database—only the tables used by the business

end users.

� Open View in SAP Lumira and Open View in Analysis Office

The last two tabs on the SAP HANA Live Browser toolbar allow you to open the

specified SAP HANA Live view in either the SAP Lumira (Figure 14.14) or the SAP

Analysis for Microsoft Office reporting tools. This allows you to create reports

easily and quickly.

Figure 14.14 Output of an SAP HANA Live View for Sales in SAP Lumira

The SAP HANA Live Browser doesn’t show you the actual models or where they are

used in your reports. You can use the SAP HANA Studio to view SAP HANA Live

models.

You also can see a list of INVALID VIEWS (Figure 14.15). Views can appear on this list

if they are missing underlying tables or dependent views. Such views won’t acti-

vate. (Activation of older calculation views is similar to the Build option of newer

calculation views.)


Figure 14.15 List of Invalid SAP HANA Live Views

Until now, we’ve discussed the developer edition of the SAP HANA Live Browser.

There is also a business user version of SAP HANA Live Browser available at http://

<SAP HANA server host>:8000/sap/hana/hba/explorer/buser.html. This version

only allows you to find a model, preview the content, and open the view in SAP

Lumira or SAP Analysis for Microsoft Office. It’s meant for nontechnical users,

such as functional consultants or specialists.

Advanced features, such as generating SAP Landscape Transformation Replication

Server files, viewing broken models, tagging a model, or opening graphical cross

references, aren’t available in the business user edition of the SAP HANA Live

Browser.

SAP HANA Live Extension Assistant: Modify Views

Earlier in the chapter, Figure 14.5 showed an example of an SAP HANA Live view,

which is similar to any other view in SAP HANA. There are two ways to modify SAP

HANA Live views:

� SAP HANA Studio

We can use the SAP HANA Studio to modify, preview, test, debug, and trace SAP


HANA Live information models. This is similar to the way we modify SAP HANA

views we created in previous chapters by using SAP Web IDE for SAP HANA.


This tool can also be used to modify SAP HANA Live views, as shown in Figure

14.16.

Figure 14.16 SAP HANA Live Extension Assistant in the SAP HANA Studio

SAP HANA Live views don’t expose all their attributes and measures; SAP has care-

fully chosen a subset of these elements for the most common business processes.

However, there are many hidden fields. You can use the SAP HANA Live extension

assistant to change which fields are hidden or shown in a particular view.

Note

Don’t delete the fields you’re not using; just mark them as hidden. SAP HANA’s optimiza-

tion is very good, and hiding a field doesn’t increase the runtime of queries.


Note

You can only extend reuse and query views, and you can extend a particular view multi-

ple times.

The SAP HANA Live extension assistant can’t extend the following:

� A query view that contains a Union node.

� A query view that contains an Aggregation node as one of the middle nodes. An

Aggregation node as the top node is normal and allowed.

� Your own custom views. It only recognizes SAP-delivered views.

Tip

You shouldn’t extend SAP-delivered views. Make a copy, and extend that instead.

You should now have a good understanding of what SAP HANA Live offers to cus-

tomers and when to use it. Now we turn our attention to a newer VDM, namely

SAP S/4HANA embedded analytics.

SAP S/4HANA Embedded Analytics

We’ve already mentioned that SAP HANA Live has evolved into SAP S/4HANA

embedded analytics. As the advantages of SAP HANA became more apparent,

SAP created a completely new version of SAP ERP that uses only SAP HANA:

SAP S/4HANA. With this new architecture and the lessons learned, we see that SAP

HANA Live evolved into SAP S/4HANA embedded analytics.

Tip

SAP S/4HANA only runs on SAP HANA, so there is no side-by-side option for SAP S/4HANA

embedded analytics as we have with SAP HANA Live.

Logically, as more customers migrate over to SAP S/4HANA, the SAP S/4HANA

embedded analytics is taking over from SAP HANA Live.

Similar Features

Many things in SAP HANA Live work very well and were kept in SAP S/4HANA

embedded analytics. The appearance and mechanisms might have been updated,

but the essence and intent of the following features stayed the same:


� Operational analysis reporting occurs from the data in the SAP transactional

system.

� VDMs are used.

� SAP builds, provides, and maintains a large library of views for use by power

users and end users in reports and analytics.

� The SAP-provided views can be extended by customers.

� The views are built up in layers to enable reuse of lower level views.

� The lower level views aren’t open for changes by customers.

� Modelers, developers, analysts, and power user can browse and search these

models.

In Table 14.1, we see a list of features available in SAP S/4HANA embedded analytics

for the different types of users. You’ll see that many of them reflect the points we

just mentioned.

You might also have noticed a few differences, for example, ABAP for Eclipse for

the IT users.

New and Updated Features

Some aspects of SAP S/4HANA embedded analytics are completely different from

SAP HANA Live. To understand these differences, it’s good to take a look at the per-

sonas developing and creating reports and enhancements inside SAP S/4HANA.

Type of user Offerings

End users: Analyze the data and act

accordingly

� Multidimensional reports and visualiza-

tions

� SAP Smart Business key performance indi-

cators (KPIs) and analysis path

� Query Browser

� Analytical SAP Fiori apps

Power users: Enable the end users � Tooling, such as Query Designer

� View and Query Browser

� SAP Fiori KPI Modeler

IT users: Provide data in a semantic layer � CDS view maintenance: ABAP for Eclipse

Table 14.1 SAP S/4HANA Embedded Analytics Offerings for Different Types of Users


While modelers and analysts focused more on SAP BW, the people creating opera-

tional reporting and extending the functionality of the SAP ERP system have

mostly been the ABAP developers. There is more focus on the ABAP developer

with SAP S/4HANA embedded analytics.

Another point to keep in mind is where these users do their work. Both ABAP

developers and SAP BW modelers work in SAP applications. They work at the appli-

cation layer, and not in the lower database layer. Herein lies a “problem” with SAP

HANA Live as this is completely developed in the database layer. These users need

SAP HANA login details and work outside of their normal working environments

with SAP HANA Live.

The next point to look at is the fact that SAP HANA calculation views are read-only

views. Many transactional systems require actions to be taken at certain times and

when certain conditions occur. Reading and writing would work far better here.

With this in mind, SAP decided to build SAP S/4HANA embedded analytics with

ABAP CDS views.

Note

ABAP CDS and SAP HANA CDS are somewhat similar, but they aren’t the same. SAP HANA

CDS views are specific to SAP HANA. ABAP CDS can be use with other databases as well

and therefore isn’t as feature-rich as SAP HANA CDS.

Tip

The concept of CDS, which we already discussed in Chapter 5, is also part of the “SAP-sup-

plied virtual data models” exam topic area.

We can now look at some of the features in SAP S/4HANA embedded analytics that

differ from those of SAP HANA Live:

� The views are created with ABAP CDS.

� The views are built and stored in the ABAP Data Dictionary.

� All authorizations are done with standard security roles in the application. (SAP

HANA Live uses SAP HANA analytic privileges for security.)

� The ABAP CDS views are created in a text editor (not a graphical modeler) in the

ABAP for Eclipse environment. This is similar to the SAP HANA Studio, which is

also based on Eclipse.

� The focus is on developers rather than on modelers.


� Calculation views focus solely on BI scenarios, whereas ABAP CDS views can also

be used for transactional and analytical scenarios.

� ABAP CDS is very integrated with the standard transport process to get develop-

ment artifacts from a development system to production systems.

� SAP S/4HANA embedded analytics provides predelivered content in the form of

ABAP CDS views. SAP HANA Live provides SAP HANA calculation views.

The SAP S/4HANA embedded analytics VDM reflects these differences.

Virtual Data Model

In Figure 14.17, we see the VDM for SAP S/4HANA embedded analytics. Compare

this to Figure 14.4, which shows the VDM for SAP HANA Live. At first glance, you’ll

notice a lot of similarities.

Figure 14.17 VDM for SAP S/4HANA Embedded Analytics

Database Tables

SAP S/4HANA embedded analytics

Reports, Analytics,and Applications

Basic views

Composite views

Consumption views

Interface Views

CustomerExtension

CustomerExtension


The main difference seems to be the naming of the views in the different layers.

Instead of private and reuse views, we now have basic and composite views. Just as

you aren’t allowed to modify private and reuse views in SAP HANA Live, the same

restrictions apply to the basic and composite views in SAP S/4HANA embedded

analytics.

Where we had query views and customers extensions in SAP HANA Live, we have

consumption views and customer extensions in SAP S/4HANA embedded analyt-

ics.

Note

The certification exam focuses on the modeling aspects of SAP HANA. As such, the inter-

est in this chapter was more on the calculation views as used in SAP HANA Live. We spent

less time on ABAP CDS views because it’s outside the scope of SAP HANA modeling.


In this chapter, we covered the following important terminology:

� Operational reporting

Operational reporting is reporting that is done on transactional data (e.g., sales

and deliveries) to enable people to do their work today (e.g., delivering a sold

item). SAP BW reports are normally the next day only, and they don’t help with,

for example, today’s deliveries. SAP HANA Live is meant only for operational

reporting requirements in SAP operational systems such as SAP ERP.

� SAP HANA Live

SAP HANA Live consists of SAP-created calculation views (for the most part) and

several tools for working with these SAP HANA views.

� SAP HANA Live views

The SAP HANA Live views are organized in an architectural structure called the

VDM. The different SAP HANA Live views are as follows:

– Private views: These are built directly on the database tables and on other pri-

vate views. These denormalize the database source data for use in the reuse

views.

– Reuse views: These structure the data in a way that business processes can

use. These views can be reused by other views.


– Query views: These can be consumed directly by analytical tools and applica-

tions. They aren’t reused by other views. These views are used when design-

ing reports.

– Customer query views: These are created either by copying SAP-provided

query views or by creating your own query views. You can add your own fil-

ters, variables, or input parameters to these views. You can also expose addi-

tional data fields or hide fields that aren’t required.

– Value help views: These are used as lookups (i.e., dropdown lists or filter lists)

for selection lists in analytical tools and business applications. They make it

easier for users to filter data by providing a list of possible values to choose

from. Value help views aren’t used in other VDM views; they are consumed

directly by analytical tools and business applications.

� SAP HANA Live Browser

The SAP HANA Live Browser tool is a web-based (HTML5) application that

enables you to find the correct views for your reporting or analytics require-

ments.


The SAP HANA Live extension assistant tool is used to modify SAP HANA Live

views.

� SAP S/4HANA embedded analytics

SAP S/4HANA embedded analytics consists of SAP-created ABAP CDS views and

several tools for working with these views.

Practice Questions










1. Where is SAP HANA Live installed?

� A. On SAP Landscape Transformation Replication Server

� B. On the SAP ERP application server

� C. In the reporting tool

� D. In the SAP HANA database

2. Which of the following views can you extend with the SAP HANA Live exten-

sion assistant?

� A. Query views with a union

� B. Reuse views

� C. All SAP-delivered views

� D. Your own custom views

3. Which sentence best describes SAP HANA Live?

� A. Operational reporting moving back to the OLTP system

� B. Analytical reporting moving back to the OLAP system

� C. Operational reporting moving back to the OLAP system

� D. Analytical reporting moving back to the OLTP system

4. Which of the following are features of a data warehouse? (There are 3 correct

answers.)

� A. Consolidation

� B. Data governance

� C. Both OLTP and OLAP in one system

� D. Real-time reporting

� E. A full transactional history

5. What tool generates files to help configure SAP Landscape Transformation

Replication Server table replication?

� A. SAP HANA Live extension assistant

� B. Authorization Assistant

� C. SAP HANA Studio

� D. SAP HANA Live Browser


6. True or False: You can create custom query views by directly modifying SAP-

delivered query views.

� A. True

� B. False

7. True or False: When you upgrade SAP HANA Live content, you can choose to

keep your modifications.

� A. True

� B. False

8. Which of following are features associated with SAP HANA Live? (There are 2

correct answers.)

� A. A new data warehouse

� B. Hundreds of predeveloped views

� C. Web-based tools

� D. Precalculated aggregates stored in cubes

9. Which of the following terms are associated with an operational system?


� A. OLTP

� B. OLAP

� C. Transactional

� D. Data warehouse

10. True or False: SAP HANA Live replaces SAP BW.

� A. True

� B. False

11. True or False: You can view metadata of SAP HANA Live views with the busi-

ness user version of the SAP HANA Live Browser.

� A. True

� B. False


12. In which package in SAP HANA will you find the SAP HANA Live views?

� A. /sap/hana/democontent

� B. /public

� C. /sap/hba

� D. /sap/bc

13. As what are most of the views in SAP HANA Live created?

� A. Table functions

� B. Calculation views

� C. CDS data sources

� D. Cubes

14. Which of the following VDM views can be called by applications and reports?


� A. Reuse views

� B. Value help views

� C. Private views

� D. Query views

15. Which views in SAP S/4HANA embedded analytics may you extend?

� A. Composite views

� B. Query views

� C. Interface views

� D. Consumption views

16. How does SAP S/4HANA embedded analytics differ from SAP HANA Live?


� A. It uses ABAP CDS views.

� B. It uses SAP HANA calculation views.

� C. Authorizations are at the database level.

� D. Authorizations are at the application level.


17. True or False: SAP S/4HANA embedded analytics also has two architecture

deployment options like SAP HANA Live, namely embedded and side-by-side.

� A. True

� B. False



SAP HANA Live is installed in the SAP HANA database (refer to Figure 14.3).

The SAP Landscape Transformation Replication Server is only used for the side-

car scenario. You still need to install SAP HANA Live, even if you don’t have a

sidecar scenario, so this is obviously wrong.

SAP HANA calculation views need to be inside SAP HANA, as the name indi-

cates. They won’t run in the SAP ERP application server.

Reporting tools can use SAP HANA views, but these views can’t be run in the

reporting layer.


You can extend reuse and query views with the SAP HANA Live extension assis-

tant.

You can’t extend query views with a Union node or your own custom views.


SAP HANA Live can be described as “Operational reporting moving back to the

OLTP (operational) system.”

4. Correct answers: A, B, E

Data warehouse features include consolidation, data governance, and a full

transactional history. Please note that the question did not specify that this is

an SAP BW on SAP HANA system. It asked about a generic data warehouse. Hav-

ing both OLTP and OLAP in one system is only available for SAP HANA real-time

reporting.


SAP HANA Live Browser can help generate files for SAP Landscape Transforma-

tion Replication Server replication.

SAP HANA Live extension assistant is used for modifying SAP HANA Live views.


The Authorization Assistant helps with creating users and roles for SAP HANA

Live.

The SAP HANA Studio doesn’t have this functionality.


This is actually a “bad” question. It’s clear from this chapter that you should not

create custom query views by directly modifying SAP-delivered query views.

However, the question asked if you can do it. Technically, it’s possible—but

you’ll lose all your hard work during the next update of the SAP HANA Live con-

tent.


When you upgrade the SAP HANA Live content, there is no option for you to

keep your modifications. All changes to SAP-delivered content will be lost. This

is different from the process in ABAP systems, which does ask if you want to

save your modifications.

This question was very similar to the previous question. In the certification

exam, you won’t find one question providing the answer to another. The SAP

team ensures that this doesn’t happen.


SAP HANA Live consists of hundreds of predeveloped views, as well as some

web-based tools.

SAP HANA Live isn’t a new data warehouse, as we clearly specified that it

doesn’t replace SAP BW.

With SAP HANA, we don’t store precalculated aggregates in cubes. This is only

done in old non-SAP HANA data warehouses.


An operational system is the transactional (OLTP) system.


SAP HANA Live does NOT replace SAP BW. Only the operational reporting por-

tion moves back from the data warehouse to the transactional system.


You can’t view the metadata of SAP HANA Live views within the business user

version of SAP HANA Live Browser. Metadata, like table definitions, is only used

by modelers and developers. This version is meant for nontechnical users.

Takeaway Chapter 14 619


The SAP HANA Live views are in the /sap/hba subpackage. Refer to Figure 14.6

to see the SAP HANA Live content in the SAP HANA Studio.


Most of the views in SAP HANA Live are calculation views.


Query views and value help views can be called by applications and reports

(refer to Figure 14.4).

Reuse views and private views may not be called directly.


You may extend consumption views in SAP S/4HANA embedded analytics.

Query views are in SAP HANA Live.

Interface views and composite views may not be extended.


SAP S/4HANA embedded analytics differs from SAP HANA Live in that it uses

ABAP CDS views, and authorizations are at the application level.

SAP HANA Live uses SAP HANA calculation views, and the authorizations are at

the database level.


False: SAP S/4HANA embedded analytics does not have two architecture

deployment options like SAP HANA Live. It does not have a side-by-side option

because it can only be deployed on SAP HANA.

Takeaway

You should now know what SAP HANA Live is, the concepts behind it, and the

architecture options.

You can use your understanding of the VDM to choose which views can be used,

modified, or extended, and you can use the SAP HANA Live Browser tool to help

design your reports.

You also got an overview of how SAP HANA Live has evolved into SAP S/4HANA

embedded analytics and how this differs from SAP HANA Live.


Summary

In this chapter, you used your knowledge of SAP HANA modeling in a scenario in

which SAP HANA is used as a database for SAP business systems such as SAP ERP.

The difference here is that SAP has developed hundreds of information models

already, and you can tap into this vast resource to build powerful analytical

reports quickly.

This chapter completes your learning experience with SAP HANA modeling. We

wish you the best of luck with your SAP HANA certification exam!

621

The Author

Rudi de Louw is head of the SAP Co-Innovation Lab at SAP

in South Africa where he supports SAP partners in building

or improving their business ideas and systems with SAP

platforms such as SAP HANA, SAP Cloud Platform, and SAP

Leonardo. In this role, he also helps partners extend the

integration of their current business products with avail-

able and emerging SAP technologies.

Rudi has been working with SAP HANA since 2010, and he

has been involved from the outset in preparing the SAP

HANA certification exams. He has provided software and technology solutions to

businesses for more than 25 years and has a passion for sharing knowledge, men-

toring, understanding new technologies, and finding innovative ways to leverage

these skills to help individuals in their development.

623

Index

A

ABAP ......................................................................... 384

ABAP Data Dictionary ....................................... 610

Accelerated K-Means ......................................... 442

Accelerator deployment ...................................... 96

profitability analysis ........................................ 95

Acclaim platform .................................................... 36

ACID-compliant database ................................... 86

Active/active (read-enabled) mode ................ 90

Advanced DSO ......................................................... 99

Affinity propagation .......................................... 435

AFLLANG ................................................................. 449

procedure ........................................................... 450

Aggregation ........................................ 210, 217, 522

function .............................................................. 260

Aggregation node .......... 258, 270, 271, 273, 276

restricted column ........................................... 288

Algorithms ............................................................. 435

Alias ........................................................................... 267

Amazon Web Services (AWS) ......... 50, 106, 118

American National Standards

Institute (ANSI) ................................................ 340

Analytic privileges ........................... 571, 582, 586

assign to role .................................................... 571

Anonymization node ........................................ 261

Application Function Library (AFL) ............ 347,

433, 438

Application privileges ....................................... 576

Application time period tables ...................... 344

Apply privileges property ................................ 583

Apriori algorithm ................................................ 435

ArcGIS ....................................................................... 462

Architecture .......................... 71, 79, 114, 521, 542

Associated container ......................................... 557

Association algorithms ..................................... 435

Asynchronous replication .................................. 90

Attribute .................................... 181, 200, 203, 217

calculated column .......................................... 284


restrictions ........................................................ 573

Authentication ..................................................... 554

Authorization .............................................. 539, 554

Assistant ............................................................. 552

Automated Predictive Library (APL) ........... 433

B

Backups ............................................................... 87, 88

Best practices ........................................................ 215

Big data ........................................................... 390, 523

limitations ......................................................... 391

BIGINT ...................................................................... 372

Blue-green deployment .................................... 134

Bookmark questions ............................................. 67

Bottleneck ................................................................. 79

Branch logic ........................................................... 541

Bring your own language (BYOL) .................. 140

Bring-your-own-license .................................... 107

Build error .............................................................. 169

Build process ...................................... 129, 131, 175

Business Function Library (BFL) .................... 438

Business Intelligence Consumer

Services (BICS) ......................................... 113, 309

Business intelligence tools .............................. 314

BW362 ......................................................................... 43

C

C_HANADEV ............................................................ 30

C_HANAIMP ............................................................. 29

C_HANAIMP_15 .............................................. 17, 26

scoring ................................................................... 35

C_HANATEC ............................................................. 29

C4.5 decision tree ................................................ 446

Caching .................................................................... 208

Calculated column ....... 182, 279, 281, 326, 328,

345, 346

analytic view .................................................... 331

counter ................................................................ 285

create ................................................................... 282

input parameter .............................................. 299

Index624

Calculation view .............. 98, 204, 207, 235, 557

Calculation view of type cube .... 205, 217, 218,

225, 233, 271, 273

aggregation node ........................................... 276

create ................................................................... 236

with star join ................................. 217, 218, 225

Calculation view of type cube with

star join ..................................................... 233, 271

create ................................................................... 237

star join node .......................................... 262, 276

Calculation view of type dimension .......... 205,

217, 225, 233, 238, 271

create ................................................................... 237

projection node ............................................... 276

Calculation views ........... 93, 168, 207, 231, 301,

330, 450, 505

adding data sources ...................................... 241

adding nodes .................................................... 241

create ................................................................... 235

creating joins ................................................... 242

data source ....................................................... 234

default node ..................................................... 240

graph nodes ...................................................... 492

node order ......................................................... 240

output area ....................................................... 303

save ...................................................................... 247

selecting output fields .................................. 244

variable .............................................................. 297

Calculations .................................................. 216, 524

CALL statement .................................................... 359

Cardinality ....................... 186, 243, 527, 537, 544

many-to-many ................................................ 186

many-to-one .................................................... 186

one-to-many .................................................... 186

one-to-one ......................................................... 186

type ...................................................................... 186

CDS views ................................... 197, 217, 220, 610

ABAP .................................................................... 199

benefits ............................................................... 198

data sources ..................................................... 199

Certification

prefix ...................................................................... 27

track ........................................................................ 25

Characteristic ........................................................ 200

Circularstring ........................................................ 467

Classification algorithms ................................. 436

Classroom training ................................................ 40

Clients ...................................................................... 264

Client-side JavaScript ........................................ 101

Cloud Foundry ..................................................... 145

Clustering algorithm ................................ 435, 436

Column table ................................................. 89, 340

Columnar storage ...................................... 521, 544

Column-based

database ............................................................... 83

input parameter .................................... 298, 326

storage ................................................................... 83

table ........................................................................ 79

Column-oriented tables ................................... 183

Columns ................................................................. 183

CompositeProvider ............................................... 99

Compression ............................................................ 72

Conditional statement ..................................... 346

Container ................................... 134, 139, 175, 556

isolation ............................................................. 140

Contains predicate ............................................. 422

Continuous delivery .......................................... 134

Continuous integration ................ 130, 134, 137

Convex Hull .......................................................... 474

Core data services (CDS) ...... 134, 159, 181, 197,

227, 234, 270, 468

full-text index .................................................. 419

Counters .............................................. 285, 286, 326

CPU .............................................................................. 79

in parallel ............................................................. 81

speed ....................................................................... 74

CSS ............................................................................. 101

CSV file .................................................................... 399

Cubes ........................ 181, 200, 201, 217, 218, 225

Currency ................................................................. 303

conversion ........ 182, 279, 304, 305, 327, 329,

347, 365

decimal shift ..................................................... 305

decimal shift back .......................................... 305

definition options .......................................... 306

display ................................................................ 305

reverse look up ................................................ 306

source and target ........................................... 305

Customer query view ............................... 598, 613

Cypher .................................................. 490, 494, 495

D

Data

aging ............................................................... 83, 91

Index 625

Data (Cont.)

backup .................................................................... 87

category .................................................... 236, 237

compression ........................................... 521, 544

federation .......................................................... 388

filter ...................................................................... 345

foundation .................. 203, 207, 217, 226, 525

lake ....................................................................... 390

masking .............................................................. 578

mining ................................................................. 434

noise ..................................................................... 436

persistence ............................................................ 87

preview ............................................................... 164

read ............................................................ 344, 544

records ................................................................ 340

security ............................................................... 578

sets ........................................................................... 63

source ........................................................ 234, 269

storage ................................................................... 76

temperatures ....................................................... 91

type ....................................................................... 341

volume ................................................. 87–90, 125

Data Control Language (DCL) ............... 341, 371

Data Definition Language (DDL) .................. 341,

353, 371

Data Distribution Optimizer .......................... 103

Data Lifecycle Manager ..................................... 103

Data Manipulation Language

(DML) ......................................................... 341, 371

Data modeling

artifact ...................................................... 208, 217

limit and filter .................................................. 216

Data preparation algorithms .......................... 436

Data provisioning ..................................... 375, 403

concepts ............................................................. 378

tools ..................................................................... 377

Data Query Language (DQL) ........ 341, 367, 371

Data Warehouse Monitor ................................ 103

Data Warehouse Scheduler ............................. 103

Data warehouses .................................................... 76

features ............................................................... 593

Database

administrator ...................................................... 27

column-oriented ................................................ 78

deployment .......................................................... 96

migration .................................................... 96, 115

to platform ........................................................... 79

view ...................................................................... 184

Datahub ...................................................................... 63

DBSCAN clustering ............................................. 477

Debug mode ................................................. 520, 528

Debug query mode .................................... 543, 547

Decimal shift ......................................................... 330

Decision

table ..................................................................... 270

tree ........................................................................ 436

Declarative logic .................................................. 350

Dedicated disaster recovery hardware .......... 90

Default view node ............................................... 239

Definer ..................................................................... 354

Delimited identifiers ................................ 342, 371

Delta .......................................................................... 404

buffer ............................... 83, 119, 347, 365, 522

load ................................................... 381, 387, 404

merge ..................................................... 83, 84, 522

store ........................................................................ 83

Deployment ................................ 71, 129, 131, 175

accelerator ........................................................... 96

cloud .................................................................... 106

database ................................................................ 96

development platform .......................... 99, 100

scenarios .............................................. 71, 93, 118

side-by-side solution ........................................ 94

virtual machine ............................................... 106

Deprecate ....................................................... 266, 273

Derived from procedures/scalar

function input parameter .................. 299, 327

Derived from table input

parameter ................................................. 298, 326

Design-time files ................................................. 568

Design-time objects .............. 129, 131, 174, 556

Development platform deployment ............. 99

programming languages ............................ 100

SAP HANA XS ................................................... 100

Development process ............................... 129, 131

Development user .............................................. 148

DevOps ....................................... 104, 134, 137, 501

Dictionary compression ...................................... 78

Dimension

calculation view .............................................. 205

table ............................... 200, 202, 207, 208, 217

view ... 205, 207, 217, 233, 262, 271, 273, 544

Direct input parameter ............................ 298, 326

Disaster recovery .................................................... 89

Distributed database ............................................ 85

Documentation ................................................... 161

Index626

Domain fixed values ...................... 293, 326, 328

Dynamic join .................. 195, 196, 216, 244, 525

Dynamic restrictions ......................................... 575

Dynamic SQL ............................................... 351, 364

E

E_HANAAW ABAP certification ....................... 30

E_HANABW SAP BW on SAP HANA

certification ......................................................... 30

Edge processing ................................................... 502

E-learning .................................................................. 40

Elimination technique ......................................... 67

Enterprise data warehouse (EDW) ................ 101

Entity analysis ...................................................... 425

Envelope ................................................................. 474

Equidistant ................................................... 502, 508

ESRI ........................................................................... 462

Exact text search ................................................. 421

Exam

objectives .............................................................. 32

process ................................................................... 36

questions .............................................................. 64

structure ................................................................ 34

EXEC statement ................................................... 351

Explicit full-text index ...................................... 419

Expression ............................................................. 341

Expression Editor ............................. 285, 292, 541

calculated column ......................................... 283

elements ............................................................. 283

functions area .................................................. 284

operators area ................................................. 284


Extended Well-Known Binary (WKB) .......... 479

Extended Well-Known Text (WKT) .............. 479

Extract semantics ............................................... 268

Extract, transform, load (ETL) ............... 379, 403

Extraction ............................................................... 379

F

Facet ......................................................................... 200

Fact table ................. 181, 200, 202, 203, 207, 217

Fault-tolerant text search ................................ 421

Fields

hide ...................................................................... 272

original ............................................................... 272

Fields (Cont.)

output ................................................................. 244

rename ............................................................... 272

Filter ......................... 264, 291, 524, 532, 537, 584

expression ............................. 279, 292, 326, 331

operations ......................................................... 279

variable .............................................................. 294

Flat file ..................................................................... 399

use case .............................................................. 400

Flowgraph .............................................................. 448

editor ................................................................... 447

For loop ................................................................... 350

Formula ................................................................... 247

Free system .............................................................. 50

Freestyle search ................................................... 423

Full outer join .................................... 188, 189, 244

Full-text index ...... 415, 416, 418, 426, 452, 453

column ................................................................ 419

create ................................................................... 417

hidden column ................................................ 419

Function ................................................................. 341

Fuzzy text search ......... 347, 356, 365, 411, 415,

417, 421, 452

alternative names .......................................... 422

fuzzy threshold value ................................... 423

G

Geocoding ........................................... 394, 475, 508

GeoJSON ................................................................. 479

Geometry collection .......................................... 467

Geospatial processing ....................................... 463

GeoTools ................................................................. 480

Gerrit ........................................................................ 137

Git ........................................................... 135, 136, 175

GitHub ........................................................................ 58

Google ...................................................................... 414

Google Cloud Platform (GCP) .......... 50, 52, 106

Grammatical Role Analysis (GRA)

analyzer .............................................................. 428

Graph

algorithm .......................................................... 490

modeling ................................................... 459, 480

node ......................................... 256, 270, 275, 492

view and analyze ............................................ 487

vs hierarchies ................................................... 497

workspace ......................................................... 485

Index 627

Graph processing ...................................... 482, 508

build ..................................................................... 485

edge ...................................................................... 484

pattern matching ........................................... 488

vertices ................................................................ 483

Graphical data model ........................................ 123

Graphical flowgraph model ............................ 441

Graphical information model ........................... 80

GraphScript ......................................... 482, 495, 496

Grid clustering ...................................................... 477

Grouping set .......................................................... 541

Grubbs’ test algorithm ...................................... 436

H

HA100 ......................................................................... 42

HA215 ......................................................................... 43

HA300 ......................................................................... 42

HA301 ......................................................................... 42

HA400 .................................................................. 30, 43

HA450 ......................................................................... 42

Hard disk .................................................................... 74

Hardware ................................................................... 61

Hash partitioning ................................................ 540

HDB module ............................. 142, 152, 162, 175

HDI ............................................... 139, 142, 144, 549

HDI container .......................... 175, 487, 557, 558

security ............................................................... 557

hdinamespace file ............................................... 167

Hidden fields ......................................................... 245

Hierarchy .......................... 181, 213, 214, 279, 308

composite ID .................................................... 325

create ................................................................... 309

leveled ................................................................. 325

orphan nodes ................................................... 311

parent-child ...................................................... 312

semantics node ................................................ 309

shared .................................................................. 314

span tree ............................................................. 325

SQL access .......................................................... 314

temporal ............................................................. 325

value help .......................................................... 314

Hierarchy function ................................... 261, 316

columns .............................................................. 321

data ...................................................................... 318

generate ............................................................. 319

navigate ............................................................. 322

Hierarchy function (Cont.)

node ............................................................ 279, 317

SQL ........................................................................ 325

High availability ........................................... 86, 120

shared disk storage ........................................... 86

HOHAM1 ................................................................... 42

Horizontal aggregation ........................... 506, 508

HTML5 ............................................................ 100, 101

Hybrid cloud ................................................ 107, 115

I

Identifier ........................................................ 341, 342

Identity provider (IdP) ...................................... 141

system ................................................................. 555

if() ............................................................................... 284

Imperative logic ................................ 350, 368, 541

Implicit full-text index ..................................... 419

Index server .................................................... 88, 120

InfoCube .................................................................... 98

InfoObject .................................................................. 99

Information model

building .............................................................. 162

concepts ............................................................. 181

refactoring ..................................... 129, 170, 175

techniques ......................................................... 524

use and build .................................................... 206

Information modeler ........................................... 27

Information views ..................................... 233, 573

characteristics ................................................. 363

data source ....................................................... 273

layered ................................................................ 572

parameterized ................................................. 340

performance ..................................................... 544

Infrastructure as a service (IaaS) .......... 106, 139

Initial load ........................................... 380, 386, 404

In-memory ......................................................... 78, 82

data movement .................................................. 80

technology ................................. 71, 73, 114, 543

Inner join ....... 188, 189, 191, 216, 218, 226, 243

Input parameter .................... 182, 279, 297, 300,

302, 326

create ................................................................... 298

date ...................................................................... 300

expression ......................................................... 299

mapping ............................................................. 300

type .............................................................. 298, 326

Index628

Insert-only ................................................................ 84

principle ............................................................. 522

International Organization for

Standardization (ISO) ................................... 340

Internet of Things (IoT) ......... 130, 397, 498, 523

Intersect .................................................................. 213

Intersect node ............................................. 256, 270

Interval ........................................................... 294, 326

Invoker .................................................................... 354

security ............................................................... 372

J

Java Database Connectivity (JDBC) .............. 113

JavaScript ....................................................... 100, 134

server-side ......................................................... 101

Jenkins ..................................................................... 137

Join .................. 182, 184, 186, 190, 216, 231, 242,

363, 527

basic ..................................................................... 188

dynamic join .................................................... 195

node ......................................... 241, 249, 262, 270

performance ..................................................... 544

referential join ................................................. 191

relocation .......................................................... 390

self-join ............................................................... 189

spatial join ........................................................ 194

star join .............................................................. 204

temporal join ................................................... 193

text join .............................................................. 192

type ...................................................................... 187

K

Keep flag ................................................................. 246

Kerberos ............................................... 563, 583, 586

Key field .................................................................. 184

Key figure ............................................................... 200

K-means .................................................................. 435

k-means clustering ............................................. 476

L

Language ....................................................... 354, 358

detection ................................................... 417, 421

Lateral join ............................................................. 197

Lazy load .......................................................... 89, 125

Left outer join ................ 188, 189, 193, 216, 226,

243, 544

Level hierarchy .... 214, 215, 308, 310, 328, 329

geographic ........................................................ 310

Linestring ............................................................... 467

Linguistic text search ........................................ 421

Load .......................................................................... 380

Log

backup ............................................................ 88, 89

buffer ...................................................................... 88

volume ......................................................... 88, 125

Log replication ........................................................ 90

Logical data warehouse .................................... 391

Loop ....................................................... 368, 372, 541

M

Machine learning ....................................... 434, 523

Machine learning model .................................. 446

Managed cloud as a service (McaaS) ........... 106

Many-to-many cardinality .............................. 186

Many-to-one cardinality .................................. 186

Mapping property ........................... 245, 272, 276

Master data ................................ 204, 217, 226, 271

Materialized view ................................................ 185

Measures ................ 181, 200, 203, 217, 219, 226

calculated column ......................................... 284


Memory block ......................................................... 88

Mercator ................................................................. 463

Microservices ..................................... 130, 135, 141

Microsoft Azure ..................................... 50, 52, 106

Microsoft Excel .................................................... 122

Minus node .................................................. 256, 270

Minus operation .................................................. 213

Modeling

best practices ................................................... 365

process ....................................................... 129, 131

role .............................................................. 565, 572

tools ..................................................................... 129

Model-view-controller (MVC) ........................ 101

Monitoring role ................................................... 565

Moore’s Law ............................................................. 73

mta.yaml ................................................................ 153

Multi cloud ............................................................ 108

Multicore CPUs ........................................... 521, 543

Multidimensional expression (MDX) ......... 309

Index 629

Multilevel aggregation ...................................... 362

Multilinestring ..................................................... 467

Multiple-choice question .................................... 65

Multiple-response questions ............................ 65

Multipoint .............................................................. 467

Multipolygon ........................................................ 467

Multistore table ............................................ 92, 121

Multi-target application (MTA) ..................... 142

Multi-target replication ....................................... 91

Multitenancy ........................................................ 105

Multitenant database container (MDC) .... 104,

234, 270

N

Namespace ................................................... 151, 166

Nodes .............................................................. 231, 248

Non-equi join ........................................................ 250

NoSQL databases .................................................... 86

NULL ...................................................... 342, 367, 371

O

Object Linking and Embedding Database

for Online Analytical Processing ............. 113

Object privileges ........................................ 570, 582

OData ....................................................... 42, 101, 400

service .................................................................. 422

OLAP ............................................... 77, 116, 521, 592

OLTP ................................................ 77, 116, 521, 592

One-to-many cardinality ................................. 186

One-to-one cardinality ...................................... 186

Open Database Connectivity (ODBC) .......... 112

Open Geospatial Consortium

(OGC) ......................................................... 462, 479

Open ODS view ........................................................ 99

openSAP ..................................................................... 48

certification ......................................................... 48

Operating system ................................................... 61

Operational reporting ............................. 612, 614

Operator ........................................................ 288, 341

Optimization ......................................................... 519

best practices .................................................... 536

tools ..................................................................... 525

Optimize calculation views ............................. 537

Outlier ...................................................................... 436

Outlier detection algorithms ......................... 436

Output field .................................................. 184, 244

P

Package privileges ............................................... 577

Page manager ................................................ 88, 125

Parallel execution ............................................... 359

Parallelism ...................................................... 81, 522

Parameter ............................................................... 359

Parent-child hierarchy .................. 214, 215, 224,

308, 310

Partitioned table ..................................................... 86

Partitioning .................................................... 86, 539

Performance .......................................................... 521

enhancements ................................................. 519

Performance analysis mode ....... 520, 526, 542,

546, 547

Persistence layer .......................... 71, 85, 114, 199

PL/SQL ..................................................................... 340

Plan graph .............................................................. 532

Plan trace ................................................................ 547

Platform as a service (PaaS) .................... 106, 135

Point ......................................................................... 467

Point of interest (POI) ........................................ 465

Polygon ................................................................... 467

PostGIS .................................................................... 479

Predicates ...................................................... 341, 469

list ......................................................................... 469

tips ........................................................................ 470

Predictive ................................................................ 432

Predictive Analysis Library (PAL) ....... 432–435,

447, 452, 454

algorithms ......................................................... 436

Predictive modeling ........................ 411, 432, 440

data source ....................................................... 442

data target ........................................................ 442

predictive analysis node .............................. 442

tasks ..................................................................... 440

Preprocessing algorithms ................................ 436

Primary storage ...................................................... 87

Private cloud ................................................ 107, 115

Private view .................................................. 597, 612

Privileges ............................................. 549, 553, 582

analytic privilege ............................................ 571

application privilege ..................................... 576

object privilege ................................................ 570

Index630

Privileges (Cont.)

system privilege .............................................. 569

type ...................................................................... 569

Procedure ................................... 358, 362, 366, 368


create ................................................................... 358

parameter .......................................................... 359

read-only ............................................................ 361

Project

create your own ................................................. 62

example ................................................................. 61

Projection ............................................ 209, 217, 345

Projection node ................................ 257, 270, 328

Proof-of-concept (POC) ..................................... 399

Proximity search ................................................. 425

Proxy table ............................................................. 388

Pruning configuration table ........................... 253

Public cloud ........................................ 106, 115, 124

Q

Query ....................................................................... 341

Query unfolding .................................................. 541

Query view .......................................... 598, 599, 613

R

R integration ......................................................... 433

R language ..................................................... 100, 372

RAM ............................................................................. 75

Range ............................................................... 294, 326

partitioning ...................................................... 540

Rank node ........................................... 258, 270, 273

dynamic partition element ........................ 274

sorting ................................................................. 258

Ranking .......................................................... 209, 217

Reads SQL data ..................................................... 358

Real-time

computing ................................................ 521, 543

data ...................................................................... 208

reporting ............................................................ 185

Recommender systems .................................... 436

Recursive table ..................................................... 190

Red Hat Enterprise Linux ................................... 61

Refactoring ................................ 129, 170, 175, 176

errors ................................................................... 173

Referential integrity ................................. 191, 226

Referential join ............. 190–192, 216, 218, 226,

227, 243, 277, 525

star join .............................................................. 204

Regression algorithms ...................................... 435

Relationship direction ...................................... 484

Replication ...................................................... 95, 381

Report writers .......................................................... 27

Reporting system ................................................ 592

REST .......................................................... 42, 101, 401

Restricted column .................. 279, 286, 287, 326

operator ............................................................. 288

using calculated columns ........................... 290

Restrictions ........................................................... 573

Returns .................................................................... 353

Reuse view .......................................... 598, 599, 612

Reverse geocoding .............................................. 475

Right outer join .............. 188, 189, 219, 226, 244

Roles ......................... 549, 553, 557, 565, 581, 585

assign to user ................................................... 577

composite role ........................................ 553, 581

create ................................................................... 565

template role .................................................... 565

Round robin partitioning ................................ 540

Row-based

database ............................................................... 83

storage ................................................................... 83

table ........................................................................ 79

Row-oriented tables ........................................... 183

Rows ......................................................................... 183

storage ................................................................ 522

Runtime objects ............................... 129, 131, 556

naming ............................................................... 166

Runtime version ................................................. 174

S

Sales forecasting .................................................. 432

Sandbox .................................................................. 399

SAP Analysis for Microsoft Office ....... 110, 605

SAP Analytics Cloud ........................ 108, 122, 126

SAP Basis .................................................................... 29

SAP Business Suite ...................................... 99, 385

SAP Business Warehouse (SAP BW) ..... 96, 202,

383, 551

SAP BusinessObjects Web Intelligence ...... 110

SAP BW on SAP HANA .................. 43, 97, 99, 585

SAP BW/4HANA ...................................................... 99

Index 631

SAP C/4HANA ....................................................... 106

SAP Cloud Appliance Library ...................... 52, 53

SAP Cloud Application Programming

Model .................................................................. 200

SAP Cloud Platform ................... 50, 51, 106, 107,

129, 524

SAP Community ..................................................... 46

SAP CRM .................................................................. 551

SAP Crystal Reports .................................. 110, 122

SAP Data Quality Management ..................... 384

SAP Data Services ............................. 383, 384, 405

SAP Developer Center .......................................... 50

SAP Digital Boardroom ..................................... 109

SAP Education .......................................................... 39

SAP Enterprise Architecture Designer ........ 156

SAP ERP .......................................................... 551, 594

SAP extractor .............................................. 382, 404

SAP Fiori .................................................................. 101

SAP HANA

as a database ..................................... 96, 98, 595

as a development platform ........................... 99

as a platform .................................................... 115

as a side-by-side accelerator

solution ................................................... 94, 596

as a virtual machine ...................................... 106

as an accelerator ............................................... 96

BI tools ................................................................ 111

client .................................................................... 112

cloud .......................................................... 104, 106

create calculation view ................................ 236

data provisioning ........................................... 377

flowgraph .......................................................... 475

for series data ................................................... 503

information models ...................................... 157

project ................................................................. 151

reference guides ................................................. 45

Security Guide ..................................................... 45

SQLScript Reference .......................................... 45

training courses .......................................... 39, 41

Troubleshooting and Performance

Analysis Guide ............................................... 45

SAP HANA 1.0 as a development

platform ............................................................. 100

SAP HANA Academy ............................................. 46

SAP HANA Business Function Library (BFL)

Reference .............................................................. 45

SAP HANA certifications ..................................... 28

all exams ............................................................... 31

associate level ..................................................... 28

specialist level ..................................................... 30

SAP HANA cockpit ....... 145, 148, 149, 535, 543,

547, 556, 567

security ............................................................... 562

SAP HANA Data Management Suite ............ 403

SAP HANA database explorer .... 162, 163, 175,

318, 486, 530

SAP HANA deployment infrastructure ...... 139

SAP HANA Developer Guide .............................. 45

SAP HANA Developer Quick Start Guide ...... 45

SAP HANA Enterprise Cloud ........ 106, 107, 115

private cloud .................................................... 107

SAP HANA Interactive Education

(SHINE) .............. 45, 56, 61, 199, 262, 356, 480

application ........................................................... 59

data sets ................................................................ 63

SAP HANA Live .............. 547, 583, 585, 591, 593,

597, 610, 614

administration ................................................ 600

architecture ............................................. 595, 596

benefits ............................................................... 595

definition ........................................................... 591

reporting in transactional systems ......... 592

security ............................................................... 552

tags ....................................................................... 604

views ....................................... 589, 599, 606, 612

SAP HANA Live Browser ................ 589, 600, 613

all views .............................................................. 601

invalid views ..................................................... 605

toolbar ................................................................ 602

SAP HANA Live extension assistant ........... 589,

600, 606, 613, 614

restrictions ........................................................ 608

SAP HANA Modeling Guide ............................... 44

SAP HANA Predictive Analysis Library (PAL)

Reference .............................................................. 45

SAP HANA remote data sync .......................... 398

SAP HANA smart data access ......................... 388

adapter ............................................................... 389

virtual tables .................................................... 390

SAP HANA smart data integration ...... 392, 393

SAP HANA smart data quality ..... 392, 394, 475

SAP HANA spatial services .............................. 480

Index632

SAP HANA SPS 04 .... 85, 91, 151, 163, 197, 200,

237, 240, 255, 259, 302, 303, 325, 344, 348,

350, 362, 364, 399, 424, 434, 437, 447, 465,

468, 474, 476, 480, 497, 535, 536, 561, 566

SAP HANA streaming analytics ............ 395, 396

SAP HANA Studio ................................................ 556

SAP HANA views .................................................. 171

SAP HANA XS .................. 100, 101, 138, 144, 162

application privileges ................................... 576

DB Utilities JavaScript API Reference ........ 45

JavaScript API Reference ................................ 45

JavaScript Reference ........................................ 45

SAP HANA XSA ......... 55, 56, 100, 138, 143, 144,

162, 361, 422, 549

administration tool ....................................... 562

architecture ............................................. 129, 131

Cockpit ................................................................... 57

development users ......................................... 559

runtime ............................................................... 142

security concepts ............................................ 554

SAP HANA, express edition .............. 50, 56, 136

local system ......................................................... 54

SAP Help .................................................................... 43

SAP Landscape Transformation

Replication Server ................................. 385, 596

trigger-based replication ............................ 385

SAP Learning Hub .................................................. 41

SAP Leonardo ............................................... 397, 523

SAP Lumira ..................................................... 62, 605

SAP Lumira, designer edition ............... 110, 122

SAP Lumira, discovery edition ............. 110, 122

SAP NetWeaver ....................................................... 75

SAP Predictive Maintenance and

Service ................................................................. 523

SAP Replication Server ............................. 387, 388

SAP resources .......................................................... 43

SAP S/4HANA ...................... 82, 93, 101, 123, 585

SAP S/4HANA embedded analytics .... 608, 613

new features ..................................................... 609

SAP HANA Live ................................................ 608

SAP SQL Anywhere ............................................. 398

SAP SQL Data Warehousing ................... 102, 104

SAP SuccessFactors ............................................. 106

SAP Vora ........................................................ 401, 402

SAP Web IDE for SAP HANA ........... 58, 129, 131,

151, 175, 300, 448, 475, 487, 527, 556

output fields ..................................................... 244

SAP Web IDE for SAP HANA (Cont.)

roles ..................................................................... 567

UI .......................................................................... 158

SAPUI5 .............................................. 27, 43, 100, 101

Savepoint ................................................. 88, 89, 125

Scalable Vector Graphic (SVG) ....................... 479

Scalar function .................................. 337, 353, 354

Scale-out architecture .......................................... 86

Schema ........................................................... 556, 558

SCORE() function ................................................. 422

Scriptserver ........................................................... 438

Search ...................................................................... 417

Seasonal pattern .................................................. 432

Secondary storage ................................................. 87

Security ................................................ 351, 539, 549

concepts .................................................... 551, 553

HDI ....................................................................... 554

SAP HANA as a database ............................ 551

SAP HANA as a platform .................... 552, 583

single sign-on ................................................... 563

user and role management ........................ 562

Segmentation ....................................................... 435

Segregation of duties ........................................ 578

SELECT * ................................................ 344, 367, 372

Self-join .......................................................... 189, 216

Semantics ............................................................... 213

Semantics node ................................ 263, 270–272

columns tab ...................................................... 266

hide fields .......................................................... 267

input parameter ............................................. 297

renaming fields ............................................... 266

session client .................................................... 266

top node ............................................................. 239

variable .............................................................. 294

view properties tab ........................................ 264

Sentiment analysis .................................... 425, 453

Sequential execution ........................................ 359

Series data ..................................................... 498, 499

examples ............................................................ 500

modeling ................................................... 459, 498

Series tables ........................................................... 505

Servers ........................................................................ 86

Session variable .......................................... 302, 303

Set-oriented .................................................. 340, 366

Shortest path algorithm .......................... 490, 492

Show lineage ................................................ 172, 269

Index 633

Side-by-side deployment .................................... 94

advantages ........................................................... 95

blank system ........................................................ 95

Single sign-on ....................................................... 583

Single value .................................................. 294, 326

Slow disk .................................................................... 77

Smart meter ........................................................... 500

Social network analysis algorithms ............. 436

Software ..................................................................... 61

Software as a service (SaaS) ............................. 106

Space ............................................................... 151, 560

development ..................................................... 145

Spatial

clustering ........................................................... 476

data type ......................................... 347, 365, 466

data type, supertype ...................................... 467

expressions ........................................................ 472

functions ............................................................ 478

import data ....................................................... 479

join ........................ 194, 216, 244, 274, 277, 469

join type .............................................................. 347

modeling .................................................. 459, 462

point ..................................................................... 466

processing ....................................... 459, 463, 508

Spatial reference system (SRS) ....................... 464

Spatial reference system identifier

(SRID) ................................................................... 464

Spatial TREAT expression ................................ 467

SQL ....................................... 100, 337, 340, 365, 519

analytic privileges ................................ 266, 584

conditional statement .................................. 346

creating calculated column ....................... 345

creating projections ...................................... 345

creating tables ................................................. 342

creating union ................................................. 346

Data Definition Language .......................... 341

Data Manipulation Language .................. 341

dynamic ........................................... 351, 368, 372

Expression Editor ............................................ 283

filter data ........................................................... 345

guidelines ........................................................... 541

language ............................................................ 340

query .................................................................... 532

reading data ..................................................... 344

security ..................................................... 354, 358

SQL (Cont.)

set-oriented ....................................................... 340

view ................................................... 185, 234, 270

SQL Analyzer ............................ 529, 531, 543, 547

SQL Console ..................... 165, 359, 530, 531, 568

SQL Editor ............................................................... 574

SQL Engine ............................................................. 266

SQL/MM .................................................................. 462

SQLScript ...... 100, 302, 337, 340, 347, 362, 372,

447, 448, 519, 541, 545, 580

compiler ............................................................. 348

declarative logic .............................................. 350

dynamic SQL ..................................................... 351

for loop ............................................................... 350

libraries ............................................................... 361

multilevel aggregation ................................ 362

optimizer ............................................................ 348

predictive model ............................................. 452

procedure ........................................................... 358

security ...................................................... 351, 352

separate statement ........................................ 347

variable ............................................................... 347

while loop .......................................................... 350

Standard deviation ............................................. 210

Standard union .................................................... 251

Star join ................................................................... 204

referential join ................................................. 204

view ............................................................. 207, 217

views .................................................................... 205

Star join node ..................................... 270, 273, 276

data foundation .............................................. 262


Statement ............................................................... 341

Static list input parameter ...................... 298, 327

Statistics algorithms .......................................... 436

Stemming ...................................................... 415, 421

Stored procedure ................................. 80, 123, 358

Strongly connected components

algorithm .................................................. 490, 494

Sum ........................................................................... 210

Supervised learning .................................. 436, 454

SUSE Linux Enterprise Server (SLES) ....... 56, 61

Synchronous ......................................................... 125

replication ............................................................ 90

System privileges ................................................ 569

System-versioned tables .................................. 343

Index634

T

Table ................................................................ 183, 198

create ................................................................... 342

data source .............................................. 234, 269

definitions ......................................................... 163

function ........................................... 337, 353, 356

function node .................................................. 261

join ....................................................................... 525

left and right .................................................... 186

link ........................................................................ 184

partitioned ................................................. 86, 534

recursive ............................................................. 190

select .................................................................... 184

split ...................................................................... 540

type ...................................................................... 444

Technical performance tuning experts ........ 27

Technical user ....................................................... 557

Template roles ...................................................... 565

modeling role ................................................... 565

monitoring role ............................................... 565

Temporal join ................ 190, 193, 194, 216, 218,

226, 277

Tenants ........................................................... 104, 274

TensorFlow ............................................................ 433

Term document matrix ................................... 416

Text analysis ............................. 411, 415, 425, 452

configuration file ........................................... 415

elements ............................................................. 430

result storage ................................................... 429

results .................................................................. 427

SAP HANA ......................................................... 429

Text index .............................................................. 416

Text join .................. 190, 192, 216, 220, 226, 244

left outer join ................................................... 193

Text mining ..................... 411, 415, 417, 430, 452

capabilities ............................................... 430, 452

configuration file ........................................... 416

index .................................................................... 416

Text processing ........................................... 411, 414

Text search ............................................................. 415

Time dimension .................................................. 238

calculation view .............................................. 237

Time series ............................................................. 436

data ...................................................................... 508

database ............................................................ 498

Time-based calculation view ................. 238, 272

Time-based hierarchy ....................................... 214

Time-dependent hierarchy ............................. 313

value help .......................................................... 314

Tokenization ................................................ 415, 421

Trace and diagnostic files ................................ 535

Training courses ....................................... 28, 41, 42

BW362 ............................................................. 30, 43

HA100 ...................................................... 29, 35, 42

HA200 ................................................................... 29

HA201 ..................................................................... 29

HA215 ............................................................... 29, 43

HA240 .................................................................... 29

HA250 .................................................................... 29

HA300 ...................................................... 29, 35, 42

HA301 .............................................................. 29, 42

HA400 ............................................................ 30, 43

HA450 ............................................................. 30, 42

HOHAM1 ............................................................... 42

UX402 .................................................................... 43

Transaction Control Language (TCL) ........... 341

Transaction manager ........................ 88, 120, 125

Transactional data .............................................. 217

Transact-SQL (T-SQL) ......................................... 340

Transform .............................................................. 379

Trigger ..................................................................... 385

Twelve-factor app ............................................... 134

U

UDF ............................................... 353, 358, 366, 368


Undelimited identifier ..................................... 342

Unicode .......................................................... 342, 386

Union .............. 184, 207, 210, 211, 217, 525, 544

all .......................................................................... 211

creating .............................................................. 346

empty behavior ............................................... 254

pruning ............................................................... 544

with constant values ........................... 211, 251

Union node ................................ 251, 270, 271, 328

constant value ................................................. 252

mapping ............................................................. 252

pruning configuration table ...................... 253

User Account and Authorization (UAA) .... 555

Users ...................................................... 549, 553, 559

SYSTEM user ..................................................... 560

Index 635

V

Value help ..................................................... 289, 301

hierarchy ............................................................ 314

view ............................................................ 598, 613

Variable ................... 279, 293, 326, 329, 330, 348

create ......................................................... 294, 295

modeling ............................................................ 294

type ............................................................. 294, 326

Variance .................................................................. 210

Versioned table .................................................... 343

Vertical aggregation ........................................... 507

Views ........................ 181–183, 216, 218, 362, 571

disappear ........................................................... 185

save ...................................................................... 185

Virtual classrooms ................................................. 40

Virtual data model (VDM) .............. 21, 589, 591,

597, 611

Virtual machines ................................................. 119

Virtual table ........................................ 234, 270, 389

vs native table .................................................. 390

VMware Player ........................................................ 56

VMware vSphere .............................. 106, 119, 124

W

Web services ....................................... 100, 400, 401

Well-Known Binary (WKB) ............................... 479

Well-Known Text (WKT) ................................... 479

WGS84 ...................................................................... 464

What’s New documentation .............................. 45

Where-used ......................................... 170, 174, 176

While loop .............................................................. 350

With results view ................................................. 359

Workspace ..................................................... 151, 157

World Geodetic System .................................... 464

X

xsa-cockpit tool .................................................... 147

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