SAP Business Data Cloud
Consolidate your fragmented data landscape with SAP Business Data Cloud! Understand the architecture and semantic foundation of this new solution and walk through onboarding, system formation, and initial activation. Data products are at the core of SAP Business Data Cloud, so get to know what they are and what they do. Then explore data and semantic modeling with SAP Datasphere, advanced data engineering with SAP Databricks, and analytics with SAP Analytics Cloud. From connecting to your existing SAP BW system to implementing intelligent applications, this guide will show you all that SAP Business Data Cloud can do.
- Get to know SAP Business Data Cloud, the new data fabric solution
- Use its components for semantic modeling, data engineering, and analytics
- Modernize SAP BW and see how to use intelligent applications
You'll learn about:
- Foundations:
Understand the end-to-end architecture, including foundation services and data products. See how SAP Business Data Cloud is provisioned and structured as a unified environment, how individual components come together into a single formation, and how the SAP Business Data Cloud cockpit serves as the control center for managing the landscape.
- Component Solutions:
Enable governed consumption of data products by providing business context, semantic enrichment, and analytical modeling with SAP Datasphere. See how SAP Databricks complements SAP Datasphere by providing scalable functionality for advanced data engineering, machine learning, and AI workloads. Then walk through SAP Analytics Cloud as the primary analytics and planning layer within SAP Business Data Cloud.
- Real-World Enterprise Environments:
Examine common adoption patterns, governance principles, and operating models that help balance agility with control. Explore how real organizations use intelligent applications in SAP Business Data Cloud to transform curated business data into ready-to-use insights.
Key Highlights:
- Architecture
- Onboarding
- System formation
- Data products
- SAP Datasphere
- SAP Databricks
- SAP Analytic Cloud
- SAP Business Warehouse (SAP BW)
- Intelligent applications
- Governance
View Full Table of Contents
- Preface
- 1 What Is SAP Business Data Cloud?
- 1.1 The Evolution of Enterprise Data: From Silos to Unified Data Fabrics
- 1.1.1 The Era of Data Silos
- 1.1.2 The Rise of Enterprise Data Warehouses
- 1.1.3 The Emergence of Data Lakes and Lakehouses
- 1.1.4 The Shift Toward Data Fabrics
- 1.1.5 The Emergence of Business Data Fabrics
- 1.2 Why Traditional Data Extraction Loses Business Context and Trust
- 1.2.1 Business Context in an SAP System
- 1.2.2 Traditional Extraction
- 1.2.3 Where Business Context Is Lost
- 1.2.4 From Lost Context to Lost Trust
- 1.3 Modern Enterprise Data Requirements and Architectural Challenges
- 1.4 SAP Business Data Cloud: Evolution of the Business Data Fabric
- 1.5 Core Value Pillars of SAP Business Data Cloud
- 1.6 Key Personas and Roles in SAP Business Data Cloud
- 1.7 Positioning SAP Business Data Cloud Within SAP’s Data, Analytics, and AI Strategy
- 1.8 Summary
- 2 Architectural Overview and the Semantic Foundation
- 2.1 The Architectural Framework: Source-to-Business-Insight Data Flow
- 2.1.1 Core Architectural Principles
- 2.1.2 End-to-End Business Data Flow
- 2.2 Key Components of SAP Business Data Cloud Architecture
- 2.2.1 SAP Business Data Cloud Cockpit
- 2.2.2 Foundation Services and SAP HANA Cloud, Data Lake Files
- 2.2.3 SAP Datasphere HDLF Object Store
- 2.2.4 Data Products
- 2.2.5 SAP HANA Cloud
- 2.2.6 SAP Datasphere
- 2.2.7 SAP Databricks
- 2.2.8 SAP Snowflake
- 2.2.9 SAP Business Data Cloud Connect
- 2.2.10 Modernizing SAP BW and SAP BW/4HANA, Private Cloud Edition
- 2.2.11 SAP Master Data Governance
- 2.2.12 SAP Analytics Cloud
- 2.2.13 Intelligent Applications
- 2.2.14 AI Foundation
- 2.3 Foundation Services for Data Ingestion, Harmonization, and Data Products
- 2.4 End-to-End Banking Transformation Scenario with SAP Business Data Cloud
- 2.4.1 Establishing a Unified Enterprise Data Foundation
- 2.4.2 Harmonizing Business Semantics with SAP Datasphere
- 2.4.3 Modernizing Existing SAP BW Investments
- 2.4.4 Enabling Enterprise AI and Advanced Analytics
- 2.4.5 Delivering Enterprise Insights with SAP Analytics Cloud
- 2.5 Summary
- 3 Getting Started with Onboarding and System Formation
- 3.1 Onboarding Concepts
- 3.1.1 Administrative Foundation
- 3.1.2 SAP for Me as the Onboarding Entry Point
- 3.1.3 Digital Onboarding Resources
- 3.1.4 Onboarding Activities in SAP Business Data Cloud
- 3.1.5 Identity and Authentication Considerations
- 3.1.6 From Onboarding to System Formation
- 3.2 Provisioning
- 3.2.1 Licensing and Capacity Units
- 3.2.2 Provisioning the SAP Business Data Cloud Core
- 3.2.3 Provisioning SAP Datasphere
- 3.2.4 Provisioning SAP Analytics Cloud
- 3.2.5 Provisioning SAP Databricks
- 3.2.6 Provisioning Guidance and Constraints
- 3.3 System Formation and Tenant Wiring
- 3.3.1 Creating a Formation
- 3.3.2 System Participation and Cardinality Rules
- 3.3.3 Tenant Wiring (Including Components)
- 3.3.4 Key Constraints and Best Practices
- 3.4 Tenant Rewiring
- 3.4.1 What Tenant Rewiring Is and Is Not
- 3.4.2 Rewiring Process Overview
- 3.4.3 Capacity Units Allocation After Rewiring
- 3.4.4 Postrewiring Integration
- 3.5 SAP Business Data Cloud Cockpit as the Control Plane
- 3.5.1 Role of the Cockpit in the SAP Business Data Cloud Architecture
- 3.5.2 Catalog and Data Product Discovery
- 3.5.3 Governance and Access Control
- 3.5.4 System Landscape and Monitoring
- 3.5.5 Intelligent Applications and Content Activation
- 3.5.6 Federated Access and Data Integration
- 3.5.7 From Control Plane to Operational Platform
- 3.6 Activation of SAP-Managed Content
- 3.7 Summary
- 4 Understanding Data Products in SAP Business Data Cloud
- 4.1 Introduction to Data Products
- 4.1.1 From Traditional Data Integration to Data Products
- 4.1.2 Why Data Products Matter in Modern Enterprise Architectures
- 4.1.3 From Data as a Service to Data as a Product
- 4.1.4 Role of Data Products in SAP Business Data Cloud
- 4.2 Core Architecture of Data Products
- 4.2.1 Business-Centric and Domain-Oriented Design
- 4.2.2 Data Products as Governed Semantic Business Assets
- 4.2.3 Stable Interfaces and Governed Consumption
- 4.2.4 Input Ports, Output Ports, and Product Interfaces
- 4.2.5 Open Ecosystem Integration
- 4.2.6 Role of the Data Product Studio
- 4.3 Metadata and Semantic Foundations
- 4.3.1 Metadata, Business Semantics, and Context Preservation
- 4.3.2 Open Resource Discovery
- 4.3.3 Core Schema Notation Interoperability Specification
- 4.4 Types of Data Products
- 4.4.1 SAP-Managed Data Products
- 4.4.2 Customer-Managed Data Products
- 4.4.3 Primary Data Products
- 4.4.4 Derived Data Products
- 4.4.5 Interface Data Products
- 4.4.6 Data Packages and Bundled Business Content
- 4.5 Data Product Discovery, Ownership, and Consumption
- 4.5.1 Roles and Personas in the Data Product Ecosystem
- 4.5.2 Domain Ownership, Governance, and Lifecycle Management
- 4.5.3 Data Product Discovery in SAP Business Data Cloud
- 4.5.4 External Consumption Through Delta Sharing
- 4.6 Data Democratization and Marketplace-Driven Data Consumption
- 4.7 Data Products for Analytics and AI
- 4.7.1 Data Products as the Foundation for Analytics
- 4.7.2 AI-Ready Business Context and Semantic Enrichment
- 4.7.3 Future Direction of Data Products in SAP Business Data Cloud
- 4.8 Summary
- 5 Data and Semantic Modeling with SAP Datasphere
- 5.1 SAP Datasphere as the Foundation of a Business Data Fabric
- 5.2 Role of SAP Datasphere in SAP Business Data Cloud
- 5.3 Architectural Positioning
- 5.4 Architectural Building Blocks of SAP Datasphere
- 5.4.1 Space Management
- 5.4.2 Data Integration and Connectivity
- 5.4.3 Semantic Onboarding
- 5.4.4 Catalog and Marketplace
- 5.4.5 Repository Explorer
- 5.4.6 Security and Administration
- 5.4.7 Data Integration Monitor
- 5.4.8 SAP Datasphere Object Store and Apache Spark Runtime
- 5.5 Data Builder: Technical Data Modeling
- 5.5.1 Data Acquisition
- 5.5.2 Data Preparation
- 5.5.3 Data Modeling
- 5.6 Business Builder: Semantic Business Modeling
- 5.6.1 Business Entities
- 5.6.2 Fact Models
- 5.6.3 Consumption Models
- 5.6.4 Authorization Scenarios
- 5.6.5 Business Scenario: Workforce Cost and Utilization Analysis Using the Business Builder
- 5.7 Data Sharing Cockpit: Central Hub for Data Provider Management
- 5.7.1 Activating or Creating a Data Provider Profile
- 5.7.2 Creating a Customer-Managed Data Product
- 5.7.3 Business Scenario: External Economic Signals Customer-Managed Data Product
- 5.8 Business Scenario: Financial Performance Analysis Using Harmonized Data Products
- 5.9 Summary
- 6 Advanced Data Engineering and AI/ML with SAP Databricks
- 6.1 Role of SAP Databricks in SAP Business Data Cloud
- 6.2 Architectural Integration
- 6.2.1 Deployment Models
- 6.2.2 Architectural Building Blocks
- 6.2.3 Integration Flow
- 6.3 Zero-Copy Data Access and Bidirectional Data Sharing
- 6.3.1 Zero-Copy Data Access
- 6.3.2 Bidirectional Data Sharing
- 6.3.3 Role of Delta Sharing
- 6.3.4 Benefits of Zero-Copy and Bidirectional Architecture
- 6.4 SAP Business Data Cloud Connect and the Open Data Ecosystem
- 6.4.1 Extending SAP Business Data Cloud Beyond SAP
- 6.4.2 Integration Model and Architecture
- 6.4.3 Integration with External Data Platforms
- 6.4.4 Data Product–Driven Integration
- 6.4.5 Governance and Security in an Open Ecosystem
- 6.4.6 Key Capabilities
- 6.4.7 Benefits of an Open Data Ecosystem
- 6.5 Data Engineering and AI Capabilities
- 6.5.1 Working with SAP Databricks
- 6.5.2 End-to-End Data Engineering and AI Flow
- 6.5.3 Data Engineering Capabilities
- 6.5.4 Advanced Analytics and Machine Learning
- 6.5.5 Machine Learning Lifecycle Management
- 6.5.6 Real-Time and Streaming Capabilities
- 6.5.7 Generative AI and Advanced AI Scenarios
- 6.5.8 Integration with SAP Business Data Cloud
- 6.5.9 Execution Boundary Across the Architecture
- 6.6 Design Considerations and Best Practices
- 6.7 Business Scenario: Enriching an SAP Data Product with AI Insights
- 6.8 Summary
- 7 Modernizing SAP BW with SAP Business Data Cloud
- 7.1 The Evolution of SAP BW: From On-Premise to a Cloud-Native Architecture
- 7.2 Challenges of Legacy SAP BW Architectures
- 7.3 Role of Modernization in SAP Business Data Cloud
- 7.4 SAP Business Data Cloud Architecture for SAP BW Modernization
- 7.5 Transition Paths for SAP BW Modernization: Lift–Shift–Innovate Framework
- 7.5.1 Lift Phase
- 7.5.2 Shift Phase
- 7.5.3 Innovate Phase
- 7.6 Modernization Boundaries and Coexistence Models
- 7.6.1 Architectural Boundaries in SAP BW Modernization
- 7.6.2 Functional Separation of Responsibilities
- 7.6.3 Coexistence Models
- 7.6.4 Decision Criteria for Boundary Definition
- 7.7 Business Scenario: Transforming SAP BW into a Data Product– Driven Enterprise
- 7.8 Summary
- 8 Analytics and Planning with SAP Analytics Cloud
- 8.1 Role of SAP Analytics Cloud in the SAP Business Data Cloud Ecosystem
- 8.2 Architectural Building Blocks of SAP Analytics Cloud
- 8.3 Data Connectivity and Modeling
- 8.3.1 Data Connectivity (Live vs. Import)
- 8.3.2 Modeling Concepts: Model Types, Dimensions, Measures, and Hierarchies
- 8.3.3 Choosing Between Datasets and Models
- 8.4 Analytics and Story Design
- 8.4.1 The Optimized Story Experience and Unified Capabilities
- 8.4.2 Visualization Best Practices
- 8.4.3 Advanced Story Design and Scripting
- 8.4.4 Ad Hoc Analysis with Data Analyzer
- 8.4.5 Executive Presentations with SAP Digital Boardroom
- 8.5 Planning and Forecasting
- 8.5.1 Planning Process in SAP Analytics Cloud
- 8.5.2 Integrated Enterprise Planning with SAP Analytics Cloud
- 8.5.3 Collaborative Planning and Workflows
- 8.5.4 Multistep Calculations and Data Actions
- 8.5.5 Seamless Planning with SAP Datasphere
- 8.5.6 Business Scenario: Integrated Supply Chain Planning with SAP Business Data Cloud
- 8.6 Augmented Analytics and AI
- 8.6.1 Automated Discovery and Predictive Intelligence
- 8.6.2 AI-Assisted Analysis and Decision Support
- 8.6.3 Natural Language Interaction and Joule
- 8.7 Security, Governance, and Collaboration
- 8.7.1 Identity Management and Single Sign-On
- 8.7.2 User and Team Management
- 8.7.3 Role-Based Access Control
- 8.7.4 Data Access Control and Row-Level Security
- 8.7.5 Data Locking and Validation Rules
- 8.7.6 Auditing and Activity Logging
- 8.7.7 Collaboration
- 8.8 Design Best Practices for SAP Analytics Cloud
- 8.8.1 Model Design
- 8.8.2 Performance Optimization
- 8.8.3 Story and Dashboard Design
- 8.8.4 Planning Design Best Practices
- 8.8.5 Governance and Content Management
- 8.8.6 Integration with SAP Datasphere and Business Data Cloud
- 8.9 Business Scenario: From Data Product to Strategic Decision
- 8.10 Summary
- 9 Delivering Business Value with Intelligent Applications
- 9.1 Intelligent Applications in SAP Business Data Cloud
- 9.2 Business Value and Strategic Benefits of Intelligent Applications
- 9.3 Intelligent Application Architecture
- 9.4 Installing SAP-Managed Intelligent Applications
- 9.5 Extending and Customizing Intelligent Applications
- 9.6 Intelligent Applications Across Lines of Business
- 9.6.1 Finance Intelligence and Cloud ERP Intelligence
- 9.6.2 People Intelligence
- 9.6.3 Supply Chain Intelligence
- 9.6.4 Spend Intelligence
- 9.6.5 Revenue Intelligence
- 9.6.6 Cross-LoB Intelligence
- 9.6.7 Intelligent Applications and Operational Decision-Making
- 9.7 Business Scenario: Enhancing a Finance Intelligence Application
- 9.8 Summary
- 10 Adoption, Governance, and the Future of SAP Business Data Cloud
- 10.1 Adoption Patterns for SAP Business Data Cloud
- 10.2 Governance Principles in a Business Data Fabric
- 10.3 Operating Models for Data Product Ownership
- 10.4 Organizational Readiness and Role Evolution
- 10.5 Future Direction of SAP Business Data Cloud
- 10.6 Summary
- The Author
- Index
