Preface

15

Target Audience

16

Objective of This Book

16

How to Read This Book

17

Acknowledgments

19

1 What Is SAP Business Data Cloud?

21

1.1 The Evolution of Enterprise Data: From Silos to Unified Data Fabrics

22

1.1.1 The Era of Data Silos

22

1.1.2 The Rise of Enterprise Data Warehouses

23

1.1.3 The Emergence of Data Lakes and Lakehouses

23

1.1.4 The Shift Toward Data Fabrics

24

1.1.5 The Emergence of Business Data Fabrics

24

1.2 Why Traditional Data Extraction Loses Business Context and Trust

25

1.2.1 Business Context in an SAP System

25

1.2.2 Traditional Extraction

26

1.2.3 Where Business Context Is Lost

27

1.2.4 From Lost Context to Lost Trust

28

1.3 Modern Enterprise Data Requirements and Architectural Challenges

29

1.4 SAP Business Data Cloud: Evolution of the Business Data Fabric

31

1.5 Core Value Pillars of SAP Business Data Cloud

34

1.6 Key Personas and Roles in SAP Business Data Cloud

37

1.7 Positioning SAP Business Data Cloud Within SAP’s Data, Analytics, and AI Strategy

40

1.8 Summary

42

2 Architectural Overview and the Semantic Foundation

43

2.1 The Architectural Framework: Source-to-Business-Insight Data Flow

44

2.1.1 Core Architectural Principles

45

2.1.2 End-to-End Business Data Flow

46

2.2 Key Components of SAP Business Data Cloud Architecture

48

2.2.1 SAP Business Data Cloud Cockpit

49

2.2.2 Foundation Services and SAP HANA Cloud, Data Lake Files

49

2.2.3 SAP Datasphere HDLF Object Store

50

2.2.4 Data Products

52

2.2.5 SAP HANA Cloud

52

2.2.6 SAP Datasphere

54

2.2.7 SAP Databricks

55

2.2.8 SAP Snowflake

56

2.2.9 SAP Business Data Cloud Connect

58

2.2.10 Modernizing SAP BW and SAP BW/4HANA, Private Cloud Edition

59

2.2.11 SAP Master Data Governance

61

2.2.12 SAP Analytics Cloud

62

2.2.13 Intelligent Applications

63

2.2.14 AI Foundation

64

2.3 Foundation Services for Data Ingestion, Harmonization, and Data Products

66

2.4 End-to-End Banking Transformation Scenario with SAP Business Data Cloud

69

2.4.1 Establishing a Unified Enterprise Data Foundation

71

2.4.2 Harmonizing Business Semantics with SAP Datasphere

71

2.4.3 Modernizing Existing SAP BW Investments

72

2.4.4 Enabling Enterprise AI and Advanced Analytics

73

2.4.5 Delivering Enterprise Insights with SAP Analytics Cloud

74

2.5 Summary

75

3 Getting Started with Onboarding and System Formation

77

3.1 Onboarding Concepts

77

3.1.1 Administrative Foundation

78

3.1.2 SAP for Me as the Onboarding Entry Point

79

3.1.3 Digital Onboarding Resources

80

3.1.4 Onboarding Activities in SAP Business Data Cloud

80

3.1.5 Identity and Authentication Considerations

81

3.1.6 From Onboarding to System Formation

82

3.2 Provisioning

82

3.2.1 Licensing and Capacity Units

83

3.2.2 Provisioning the SAP Business Data Cloud Core

83

3.2.3 Provisioning SAP Datasphere

85

3.2.4 Provisioning SAP Analytics Cloud

86

3.2.5 Provisioning SAP Databricks

89

3.2.6 Provisioning Guidance and Constraints

91

3.3 System Formation and Tenant Wiring

92

3.3.1 Creating a Formation

92

3.3.2 System Participation and Cardinality Rules

94

3.3.3 Tenant Wiring (Including Components)

95

3.3.4 Key Constraints and Best Practices

95

3.4 Tenant Rewiring

96

3.4.1 What Tenant Rewiring Is and Is Not

96

3.4.2 Rewiring Process Overview

97

3.4.3 Capacity Units Allocation After Rewiring

98

3.4.4 Postrewiring Integration

99

3.5 SAP Business Data Cloud Cockpit as the Control Plane

99

3.5.1 Role of the Cockpit in the SAP Business Data Cloud Architecture

99

3.5.2 Catalog and Data Product Discovery

100

3.5.3 Governance and Access Control

101

3.5.4 System Landscape and Monitoring

102

3.5.5 Intelligent Applications and Content Activation

102

3.5.6 Federated Access and Data Integration

103

3.5.7 From Control Plane to Operational Platform

103

3.6 Activation of SAP-Managed Content

103

3.7 Summary

105

4 Understanding Data Products in SAP Business Data Cloud

107

4.1 Introduction to Data Products

108

4.1.1 From Traditional Data Integration to Data Products

108

4.1.2 Why Data Products Matter in Modern Enterprise Architectures

109

4.1.3 From Data as a Service to Data as a Product

110

4.1.4 Role of Data Products in SAP Business Data Cloud

111

4.2 Core Architecture of Data Products

112

4.2.1 Business-Centric and Domain-Oriented Design

113

4.2.2 Data Products as Governed Semantic Business Assets

114

4.2.3 Stable Interfaces and Governed Consumption

115

4.2.4 Input Ports, Output Ports, and Product Interfaces

116

4.2.5 Open Ecosystem Integration

117

4.2.6 Role of the Data Product Studio

118

4.3 Metadata and Semantic Foundations

119

4.3.1 Metadata, Business Semantics, and Context Preservation

119

4.3.2 Open Resource Discovery

120

4.3.3 Core Schema Notation Interoperability Specification

121

4.4 Types of Data Products

122

4.4.1 SAP-Managed Data Products

123

4.4.2 Customer-Managed Data Products

126

4.4.3 Primary Data Products

130

4.4.4 Derived Data Products

131

4.4.5 Interface Data Products

133

4.4.6 Data Packages and Bundled Business Content

134

4.5 Data Product Discovery, Ownership, and Consumption

136

4.5.1 Roles and Personas in the Data Product Ecosystem

137

4.5.2 Domain Ownership, Governance, and Lifecycle Management

138

4.5.3 Data Product Discovery in SAP Business Data Cloud

139

4.5.4 External Consumption Through Delta Sharing

141

4.6 Data Democratization and Marketplace-Driven Data Consumption

142

4.7 Data Products for Analytics and AI

145

4.7.1 Data Products as the Foundation for Analytics

146

4.7.2 AI-Ready Business Context and Semantic Enrichment

146

4.7.3 Future Direction of Data Products in SAP Business Data Cloud

148

4.8 Summary

149

5 Data and Semantic Modeling with SAP Datasphere

151

5.1 SAP Datasphere as the Foundation of a Business Data Fabric

152

5.2 Role of SAP Datasphere in SAP Business Data Cloud

153

5.3 Architectural Positioning

154

5.4 Architectural Building Blocks of SAP Datasphere

156

5.4.1 Space Management

157

5.4.2 Data Integration and Connectivity

160

5.4.3 Semantic Onboarding

163

5.4.4 Catalog and Marketplace

164

5.4.5 Repository Explorer

166

5.4.6 Security and Administration

168

5.4.7 Data Integration Monitor

170

5.4.8 SAP Datasphere Object Store and Apache Spark Runtime

172

5.5 Data Builder: Technical Data Modeling

174

5.5.1 Data Acquisition

175

5.5.2 Data Preparation

179

5.5.3 Data Modeling

180

5.6 Business Builder: Semantic Business Modeling

182

5.6.1 Business Entities

183

5.6.2 Fact Models

184

5.6.3 Consumption Models

184

5.6.4 Authorization Scenarios

184

5.6.5 Business Scenario: Workforce Cost and Utilization Analysis Using the Business Builder

185

5.7 Data Sharing Cockpit: Central Hub for Data Provider Management

186

5.7.1 Activating or Creating a Data Provider Profile

188

5.7.2 Creating a Customer-Managed Data Product

191

5.7.3 Business Scenario: External Economic Signals Customer-Managed Data Product

194

5.8 Business Scenario: Financial Performance Analysis Using Harmonized Data Products

198

5.9 Summary

200

6 Advanced Data Engineering and AI/ML with SAP Databricks

201

6.1 Role of SAP Databricks in SAP Business Data Cloud

201

6.2 Architectural Integration

203

6.2.1 Deployment Models

203

6.2.2 Architectural Building Blocks

204

6.2.3 Integration Flow

205

6.3 Zero-Copy Data Access and Bidirectional Data Sharing

206

6.3.1 Zero-Copy Data Access

206

6.3.2 Bidirectional Data Sharing

207

6.3.3 Role of Delta Sharing

208

6.3.4 Benefits of Zero-Copy and Bidirectional Architecture

209

6.4 SAP Business Data Cloud Connect and the Open Data Ecosystem

210

6.4.1 Extending SAP Business Data Cloud Beyond SAP

211

6.4.2 Integration Model and Architecture

212

6.4.3 Integration with External Data Platforms

213

6.4.4 Data Product–Driven Integration

219

6.4.5 Governance and Security in an Open Ecosystem

219

6.4.6 Key Capabilities

220

6.4.7 Benefits of an Open Data Ecosystem

220

6.5 Data Engineering and AI Capabilities

221

6.5.1 Working with SAP Databricks

222

6.5.2 End-to-End Data Engineering and AI Flow

222

6.5.3 Data Engineering Capabilities

224

6.5.4 Advanced Analytics and Machine Learning

224

6.5.5 Machine Learning Lifecycle Management

224

6.5.6 Real-Time and Streaming Capabilities

225

6.5.7 Generative AI and Advanced AI Scenarios

225

6.5.8 Integration with SAP Business Data Cloud

225

6.5.9 Execution Boundary Across the Architecture

225

6.6 Design Considerations and Best Practices

226

6.7 Business Scenario: Enriching an SAP Data Product with AI Insights

228

6.8 Summary

238

7 Modernizing SAP BW with SAP Business Data Cloud

239

7.1 The Evolution of SAP BW: From On-Premise to a Cloud-Native Architecture

240

7.2 Challenges of Legacy SAP BW Architectures

241

7.3 Role of Modernization in SAP Business Data Cloud

243

7.4 SAP Business Data Cloud Architecture for SAP BW Modernization

245

7.5 Transition Paths for SAP BW Modernization: Lift–Shift–Innovate Framework

247

7.5.1 Lift Phase

248

7.5.2 Shift Phase

252

7.5.3 Innovate Phase

261

7.6 Modernization Boundaries and Coexistence Models

263

7.6.1 Architectural Boundaries in SAP BW Modernization

264

7.6.2 Functional Separation of Responsibilities

264

7.6.3 Coexistence Models

265

7.6.4 Decision Criteria for Boundary Definition

265

7.7 Business Scenario: Transforming SAP BW into a Data Product–Driven Enterprise

266

7.8 Summary

270

8 Analytics and Planning with SAP Analytics Cloud

271

8.1 Role of SAP Analytics Cloud in the SAP Business Data Cloud Ecosystem

272

8.2 Architectural Building Blocks of SAP Analytics Cloud

273

8.3 Data Connectivity and Modeling

275

8.3.1 Data Connectivity (Live vs. Import)

276

8.3.2 Modeling Concepts: Model Types, Dimensions, Measures, and Hierarchies

279

8.3.3 Choosing Between Datasets and Models

282

8.4 Analytics and Story Design

284

8.4.1 The Optimized Story Experience and Unified Capabilities

285

8.4.2 Visualization Best Practices

286

8.4.3 Advanced Story Design and Scripting

287

8.4.4 Ad Hoc Analysis with Data Analyzer

288

8.4.5 Executive Presentations with SAP Digital Boardroom

289

8.5 Planning and Forecasting

290

8.5.1 Planning Process in SAP Analytics Cloud

290

8.5.2 Integrated Enterprise Planning with SAP Analytics Cloud

291

8.5.3 Collaborative Planning and Workflows

292

8.5.4 Multistep Calculations and Data Actions

293

8.5.5 Seamless Planning with SAP Datasphere

293

8.5.6 Business Scenario: Integrated Supply Chain Planning with SAP Business Data Cloud

296

8.6 Augmented Analytics and AI

298

8.6.1 Automated Discovery and Predictive Intelligence

298

8.6.2 AI-Assisted Analysis and Decision Support

299

8.6.3 Natural Language Interaction and Joule

300

8.7 Security, Governance, and Collaboration

301

8.7.1 Identity Management and Single Sign-On

302

8.7.2 User and Team Management

302

8.7.3 Role-Based Access Control

303

8.7.4 Data Access Control and Row-Level Security

303

8.7.5 Data Locking and Validation Rules

305

8.7.6 Auditing and Activity Logging

306

8.7.7 Collaboration

306

8.8 Design Best Practices for SAP Analytics Cloud

306

8.8.1 Model Design

307

8.8.2 Performance Optimization

307

8.8.3 Story and Dashboard Design

308

8.8.4 Planning Design Best Practices

308

8.8.5 Governance and Content Management

309

8.8.6 Integration with SAP Datasphere and Business Data Cloud

309

8.9 Business Scenario: From Data Product to Strategic Decision

310

8.10 Summary

313

9 Delivering Business Value with Intelligent Applications

315

9.1 Intelligent Applications in SAP Business Data Cloud

315

9.2 Business Value and Strategic Benefits of Intelligent Applications

318

9.3 Intelligent Application Architecture

320

9.4 Installing SAP-Managed Intelligent Applications

324

9.5 Extending and Customizing Intelligent Applications

331

9.6 Intelligent Applications Across Lines of Business

337

9.6.1 Finance Intelligence and Cloud ERP Intelligence

337

9.6.2 People Intelligence

338

9.6.3 Supply Chain Intelligence

339

9.6.4 Spend Intelligence

339

9.6.5 Revenue Intelligence

339

9.6.6 Cross-LoB Intelligence

340

9.6.7 Intelligent Applications and Operational Decision-Making

340

9.7 Business Scenario: Enhancing a Finance Intelligence Application

341

9.8 Summary

345

10 Adoption, Governance, and the Future of SAP Business Data Cloud

347

10.1 Adoption Patterns for SAP Business Data Cloud

348

10.2 Governance Principles in a Business Data Fabric

352

10.3 Operating Models for Data Product Ownership

355

10.4 Organizational Readiness and Role Evolution

358

10.5 Future Direction of SAP Business Data Cloud

362

10.6 Summary

364