Basics & Architecture
What is SAP Datasphere?
- SAP’s unified data management & analytics platform
- Successor to SAP Data Warehouse Cloud (DWC)
- Combines data virtualization, data integration, modeling, governance, and cataloging
- Built on SAP BTP
Key Components
- Spaces – Logical containers for data, connections, and models
- Data Builder – For tables, views, transformations
- Business Builder – Semantic modeling layer
- Data Marketplace – Exchange of data products
- Catalog – Metadata management
- Data Integration – Replication & federation engines
Deployment
- Cloud-native
- Uses SAP HANA Cloud as the underlying DB
2. Key Features
Semantic Modeling
- Entity, Relationship, Consumption models
- Business-level definitions, measures, attributes
- Graphical modeling experience
Data Integration
- Data federation (real-time), replication, ETL
- Integration agents for non-cloud systems
- Connectivity: SAP S/4, ECC, BW, BW/4, HANA, SF, Ariba, Concur, Fieldglass, JDBC/ODBC, REST
Spaces
- Multi-tenant environment
- Controls:
- Storage
- User access
- Connections
- Data products
Data Products
- Reusable, shareable, versioned data assets
- Can be published and consumed across spaces
3. Modeling Concepts
Local vs. Remote Tables
- Remote Table
- Virtual, federated access
- Doesn’t store data
- Can be cached
- Local Table
- Physically stored in Datasphere
Views
- Analytical Models – Measures + Dimensions
- Relational Views – SQL-based modeling
- Entity Views – Semantic representation
Hierarchy Types
- Level-based
- Parent-child
- Time-dependent
4. Permissions & Security
Main Security Concepts
- Spaces → User assignments
- Roles & Privileges (Read, Write, Admin)
- Data masking & row-level filtering
- Governance via Catalog
Single Sign-On
- Supports:
- SAML
- IAS (Identity Authentication Service)
- OAuth
5. Data Integration & Connectivity
Connections Supported
- SAP S/4HANA
- SAP BW & BW/4HANA
- SAP HANA Cloud / On-premise
- SAP SuccessFactors
- SAP Ariba, Concur
- Non-SAP: SQL Server, Oracle, Snowflake, BigQuery, Redshift, MongoDB, APIs
Replication Services
- Data Provisioning Agent
- Smart Data Integration (SDI)
- Real-time change data capture (CDC)
6. BW Bridge
What is BW Bridge?
- Bridge for BW and BW/4HANA objects → Datasphere
- Use-case: customers migrating from BW
Capabilities
- Supports:
- InfoObjects
- ADSOs
- Transformations
- DTPs
- Runs in separate space
- Provides compatibility path for classic BW developers
7. Analytical Modeling
Key Points
- Rich modeling experience
- Dimensions, Measures, Associations
- Star-schema-friendly models
- Supports external consumption:
- SAP Analytics Cloud
- Power BI
- Tableau
- Looker
- Excel
8. Performance Optimization
Techniques
- Use local tables for heavy transformations
- Optimize joins & filters
- Use cache for remote tables
- Partitioning & indexing in HANA Cloud
- Prefer views over complex SQL scripts
- Use delta loads for replication
9. Common Interview Questions
- Difference between SAP Datasphere and SAP BW/4HANA
- Difference between replication and federation
- What are Spaces and how do we use them?
- What is SAP BW Bridge?
- Remote vs. Local tables
- SAP Datasphere vs. Data Warehouse Cloud
- What are Data Products?
- How do you build an Analytical Model?
- Explain the Business Builder
- How do you connect Datasphere with non-SAP sources?
- What is the role of Data Provisioning Agent?
- What is SAP Datasphere Catalog?
- What is semantic modeling?
- What security features are available?
- How do you optimize performance in Datasphere?
10. Real-Time Scenarios
Scenario 1: Pulling data from S/4HANA
- Create connection → Import ODP objects → Create remote table → Replicate if needed → Model → Consume in SAC
Scenario 2: Replacing BW System
- Use BW Bridge → Move ADSOs & transformations → Rebuild semantic layer → Use SAC for reporting
Scenario 3: Multi-source model
- Combine SAP + Non-SAP data via federation → Create analytical model → Expose as data product