Preface

19

Purpose

19

Who Should Read This Book

19

Structure of This Book

20

Part I: Introduction

20

Part II: Getting Data into SAP HANA

21

Part III: Multidimensional Modeling in SAP HANA

21

Part IV: Integrating SAP HANA with SAP Business Intelligence Tools

22

1 SAP HANA, SAP BusinessObjects BI, and SAP Data Services

27

1.1 What Is SAP HANA?

28

1.1.1 Software Layers and Features

28

1.1.2 Hardware Layers and Features

32

1.2 Business Intelligence Solutions with SAP HANA

38

1.2.1 SAP BW on SAP HANA

38

1.2.2 Native Implementation of SAP HANA for Analytics

42

1.3 SAP Business Suite on SAP HANA

57

1.4 Traditional EIM with SAP Data Services

60

1.4.1 Align IT with the Business

60

1.4.2 Establish Processes to Manage the Data

61

1.4.3 Source System Analysis

61

1.4.4 Develop a Data Model

62

1.4.5 Load the Data

62

1.5 Traditional Business Intelligence with SAP BusinessObjects BI

62

1.5.1 The Semantic Layer (Universe)

63

1.5.2 Ad Hoc Reporting

64

1.5.3 Self-Service BI

64

1.5.4 IT-Developed Content

66

1.6 Solution Architectural Overview

66

1.6.1 SAP Data Services

67

1.6.2 SAP BusinessObjects BI

70

1.6.3 SAP HANA

73

1.7 Summary

76

2 Securing the SAP HANA Environment

77

2.1 Configuring the SAP HANA Environment for Development

78

2.1.1 Introduction to the SAP HANA Repository

79

2.1.2 Configuring SAP HANA Studio

80

2.1.3 Setting Up Packages and Development Projects

87

2.1.4 Setting up Schemas in SAP HANA

95

2.2 SAP HANA Authorizations

100

2.2.1 Types of SAP HANA Privileges

101

2.2.2 Granting of Privileges and the Life Cycle of a Grant

106

2.3 User and Role Provisioning

108

2.3.1 Creating Roles (the Traditional Approach)

109

2.3.2 Creating Roles as Repository Objects

110

2.3.3 Preventing Rights Escalation Scenarios

115

2.3.4 Common Role Scenarios and Their Privileges

116

2.3.5 User Provisioning

126

2.4 SAP HANA Authentication

131

2.4.1 Internal Authentication with User Name and Password

133

2.4.2 Kerberos Authentication

135

2.4.3 SAML Authentication

136

2.4.4 Other Web-Based Authentication Methods for SAP HANA XS

137

2.4.5 Summary and Recommendations

138

2.5 Case Study: An End-to-End Security Configuration

139

2.5.1 Authentication Plan

139

2.5.2 Authorization Plan

147

2.5.3 User Provisioning Plan

150

2.6 Summary

152

3 Data Storage in SAP HANA

155

3.1 OLAP and OLTP Data Storage

155

3.1.1 The Spinning Disk Problem

157

3.1.2 Combating the Problem

157

3.2 Data Storage Components

165

3.2.1 Schemas and Users

165

3.2.2 Column-Store Tables

167

3.2.3 Row-Store Tables

172

3.2.4 Use Cases for Both Row- and Column-Store Tables

173

3.3 Modeling Tables and Data Marts

175

3.3.1 Legacy Relational OLAP Modeling

176

3.3.2 SAP HANA Relational OLAP Modeling

180

3.3.3 Denormalizing Data in SAP HANA

183

3.4 Case Study: Creating Data Marts and Tables for an SAP HANA Project

186

3.4.1 Creating a Schema for the Data Mart

187

3.4.2 Creating the Fact Table and Dimension Tables in SAP HANA

189

3.5 Summary

194

4 Preprovisioning Data with SAP Data Services

197

4.1 Making the Case for Source System Analysis

197

4.2 SSA Techniques in SAP Data Services

202

4.2.1 Column Profiling

205

4.2.2 Relationship Profiling

211

4.3 SSA: Beyond Tools and Profiling

215

4.3.1 Establishing Patterns

217

4.3.2 Looking Across Sources

219

4.3.3 Treating Disparate Systems as One

219

4.3.4 Mapping Your Data

220

4.4 Summary

222

5 Provisioning Data with SAP Data Services

223

5.1 Provisioning Data Using SAP Data Services Designer

223

5.1.1 Metadata

225

5.1.2 Datastores

227

5.1.3 Jobs

231

5.1.4 Workflows

234

5.1.5 Data Flows

244

5.1.6 Transforms

255

5.1.7 Built-In Functions

277

5.1.8 Custom Functions and Scripts

281

5.1.9 File Formats

285

5.1.10 Real-Time Jobs

288

5.2 Introduction to SAP Data Services Workbench

290

5.2.1 Building a Data Flow

293

5.2.2 Moving Data from an Existing Data Warehouse

297

5.2.3 Porting Data with the Quick Replication Wizard

297

5.2.4 Modifying Data Flows and Jobs

304

5.3 Data Provisioning via Real-Time Replication

305

5.3.1 SAP Data Services ETL-Based Method (ETL and DQ)

306

5.3.2 SAP Landscape Transformation

307

5.4 Summary

308

6 Loading Data with SAP Data Services

309

6.1 Loading Data in a Batch

309

6.1.1 Steps

309

6.1.2 Methods

319

6.1.3 Triggers

328

6.2 Loading Data in Real Time

335

6.3 Case Study: Loading Data in a Batch

340

6.3.1 Initialization

344

6.3.2 Staging

345

6.3.3 Mart

374

6.3.4 End Script

387

6.4 Case Study: Loading Data in Real Time

389

6.5 Summary

395

7 Introduction to Multidimensional Modeling

399

7.1 Understanding Multidimensional Models

400

7.2 Benefits of SAP HANA Multidimensional Modeling

404

7.2.1 Business Benefits

404

7.2.2 Technology Benefits

408

7.3 Summary

411

8 Tools and Components of Multidimensional Modeling

413

8.1 SAP HANA Studio

413

8.1.1 Systems View

417

8.1.2 Quick Launch View

418

8.2 Schemas

420

8.3 Packages

423

8.4 Summary

426

9 Creating SAP HANA Information Views

427

9.1 Attribute Views

427

9.1.1 Creating an Attribute View

429

9.1.2 Defining Properties of an Attribute View

431

9.1.3 Creating Hierarchies

439

9.1.4 Saving and Activating the Attribute View

442

9.2 Analytic Views

444

9.2.1 Creating an Analytic View

445

9.2.2 Defining Properties of an Analytic View

447

9.2.3 Saving and Activating the Analytic View

459

9.3 Calculation Views

460

9.3.1 Creating a Calculation View

461

9.3.2 Defining a Graphical Calculation View

466

9.3.3 Defining a Script-Based Calculation View

474

9.4 Summary

478

10 Multidimensional Modeling in Practice

479

10.1 Data Processing in SAP HANA

479

10.1.1 Normalized Data versus Denormalized Data

480

10.1.2 Data Modeling versus Multidimensional Modeling

485

10.1.3 Managing Normalized Data in SAP HANA

487

10.2 Case Study 1: Modeling Sales Data to Produce Robust Analytics

490

10.2.1 Creating the Supporting Attribute Views

490

10.2.2 Creating Analytic Views

508

10.3 Case Study 2: Building Complex Calculations for Executive-Level Analysis

515

10.3.1 Creating the Package

516

10.3.2 Creating the Calculation View

518

10.3.3 Defining the Calculation View

520

10.4 Summary

533

11 Securing Data in SAP HANA

535

11.1 Introduction to Analytic Privileges

536

11.1.1 What are Analytic Privileges?

536

11.1.2 Types of Analytic Privileges

537

11.1.3 Dynamic vs. Static Value Restrictions

538

11.2 Creating Analytic Privileges

540

11.2.1 Traditional Analytic Privileges

540

11.2.2 SQL-Based Analytic Privileges

556

11.3 Applying Analytic Privileges

561

11.3.1 Applying Analytic Privileges to Information Views

561

11.3.2 Interaction of Multiple Analytic Privileges and Multiple Restrictions

563

11.3.3 Interaction of Multiple Information Views with Analytic Privileges

564

11.4 Case Study: Securing Sales Data with Analytic Privileges

567

11.4.1 Overview and Requirements

568

11.4.2 Implementation Strategy

569

11.4.3 Implementation Examples

570

11.5 Summary

581

12 Building Universes for SAP HANA

585

12.1 SAP HANA and the Universe

587

12.1.1 When to Use a Universe with SAP HANA

590

12.1.2 Connecting Universes to SAP HANA

592

12.2 Manually Building UNX Universes for SAP HANA

600

12.2.1 Creating Relational Connections

601

12.2.2 Creating OLAP Connections

610

12.2.3 Testing Connections Using the Local or Server Middleware

612

12.2.4 Creating Projects

613

12.2.5 Designing the Data Foundation

617

12.2.6 Designing the Business Layer

632

12.2.7 Publishing the Universe

639

12.3 Automatically Generating UNX Universes for SAP HANA

642

12.3.1 Creating a Local Connection

642

12.3.2 Selecting Information Views

645

12.3.3 Reviewing the Data Foundation and Business Layer

648

12.3.4 How SAP HANA Metadata Impacts the Process

655

12.4 The SAP HANA Engines in Universe Design

656

12.4.1 SAP HANA Join Engine

658

12.4.2 SAP HANA OLAP Engine

659

12.4.3 SAP HANA Calculation Engine

660

12.5 Case Study: Designing a Universe to Support Internet Sales Data

662

12.5.1 Creating the Universe Connection and Project

662

12.5.2 Designing the Data Foundation

663

12.5.3 Designing the Business Layer

671

12.5.4 Publishing the Universe

688

12.6 Summary

689

13 Predictive Analytics with SAP HANA

691

13.1 Predictive Analysis and SAP HANA: The Basics

692

13.1.1 The Predictive Analysis Process

696

13.1.2 When to Use Predictive Analytics

703

13.1.3 Predictive Tools Available in SAP HANA

706

13.2 Integrating with SAP HANA

712

13.2.1 Installing the Application Function Libraries

712

13.2.2 Deploying Rserve

712

13.2.3 Leveraging R and PAL to Produce Predictive Results

713

13.2.4 Installing SAP Predictive Analysis

714

13.2.5 User Privileges and Security with SAP Predictive Analysis

714

13.3 Integrating with SAP BusinessObjects BI

716

13.3.1 Exporting Scored Data Back to Databases

716

13.3.2 Exporting Algorithms

717

13.4 Case Study 1: Clustering Analysis

719

13.4.1 Preparing the Data

720

13.4.2 Performing Clustering Analysis

724

13.4.3 Implementing the Model

732

13.5 Case Study 2: Product Recommendation Rules

738

13.5.1 Preparing the Data

738

13.5.2 Performing Apriori Analysis

738

13.5.3 Implementing the Model

745

13.6 Summary

750

14 Professionally Authored Dashboards with SAP HANA

751

14.1 SAP HANA as a Data Source for SAP BusinessObjects Dashboards

754

14.2 SAP HANA as a Data Source for SAP BusinessObjects Design Studio

759

14.2.1 Connecting to SAP BW on SAP HANA

760

14.2.2 Connecting Directly to SAP HANA Data Sources

760

14.2.3 Connecting to the SAP HANA XS Engine

761

14.2.4 Consuming the SAP HANA Connections

763

14.3 Case Study: Exploring Data with SAP BusinessObjects Design Studio on Top of SAP HANA

764

14.3.1 Gathering Requirements

764

14.3.2 Laying Out the Components

765

14.3.3 Connecting to SAP HANA

766

14.4 Summary

769

15 Data Exploration and Self-Service Analytics with SAP HANA

771

15.1 SAP HANA as a Data Source for SAP BusinessObjects Explorer

772

15.1.1 Exploring and Indexing

773

15.1.2 Connecting SAP BusinessObjects Explorer to SAP HANA

776

15.1.3 Creating an Information Space on SAP HANA

778

15.2 SAP HANA as a Data Source for SAP Lumira

780

15.2.1 Online Connectivity

783

15.2.2 Offline Connectivity

784

15.3 Case Study: Exploring Sales Data with SAP Lumira on Top of SAP HANA

789

15.3.1 Business Requirements

790

15.3.2 Planned Solution

790

15.4 Summary

796

16 SAP BusinessObjects Web Intelligence with SAP HANA

797

16.1 Connecting SAP BusinessObjects Web Intelligence to SAP HANA

797

16.2 Report Optimization Features with SAP HANA

800

16.2.1 Usage of JOIN_BY_SQL

800

16.2.2 Merged Dimensions versus Analytic/Calculation Views

803

16.2.3 Query Drill

804

16.2.4 Query Stripping

806

16.3 Case Study: Exploring Sales Data with SAP BusinessObjects Web Intelligence on Top of SAP HANA

808

16.4 Summary

814

17 SAP Crystal Reports with SAP HANA

815

17.1 SAP HANA as a Data Source for SAP Crystal Reports

817

17.1.1 Configuring ODBC and JDBC Connections

819

17.1.2 Using SAP BusinessObjects IDT Universes

822

17.1.3 Using SAP BusinessObjects BI Relational Connections

824

17.1.4 Direct OLAP Connectivity to Analytic and Calculation Views

826

17.2 Case Study: Exploring Data with SAP Crystal Reports on Top of SAP HANA

831

17.2.1 Connecting to Data

832

17.2.2 Designing the Query

833

17.2.3 Limiting Query Results with Filter

834

17.2.4 Formatting the Report Display

835

17.3 Summary

836

Appendices

837

A Source System Analysis with SAP Information Steward Data Insight

837

A.1 Column Profiling

841

A.2 Address Profiling

843

A.3 Dependency Profiling

845

A.4 Redundancy Profiling

846

A.5 Uniqueness Profiling

847

A.6 Summary

848

B The Authors

849