SAP SAP BW Syllabus

SAP BW syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for SAP BW in SAP, not a summary of it.

1. 1

   Introduction to SAP BW

   3 topics

   • Overview of SAP BW
     • What is SAP BW?
     • History and Evolution
     • Key Features and Benefits
   • SAP BW Architecture
     • Core Components
     • Data Flow
     • Integration with Other SAP Modules
   • SAP BW vs. SAP HANA
     • Differences and Similarities
     • When to Use Each
2. 2

   Data Modeling in SAP BW

   4 topics

   • InfoObjects
     • Characteristics
     • Key Figures
     • Attributes and Texts
   • InfoProviders
     • DSOs (Data Store Objects)
     • Cubes
     • MultiProviders
     • CompositeProviders
   • DataStore Objects (DSOs)
     • Standard DSOs
     • Write-Optimized DSOs
     • Direct Update DSOs
   • Advanced DataStore Objects (aDSOs)
     • Types and Usage
     • Configuration Options
3. 3

   Data Extraction in SAP BW

   3 topics

   • Source Systems
     • SAP Source Systems
     • Non-SAP Source Systems
   • Data Extraction Techniques
     • Full Load
     • Delta Load
   • ETL Process
     • Extraction
     • Transformation
     • Loading
4. 4

   Data Loading and Transformation

   3 topics

   • Process Chains
     • Creating Process Chains
     • Monitoring and Managing Process Chains
   • Data Transfer Processes (DTPs)
     • Creating DTPs
     • Configuring DTPs
   • Transformations
     • Transformation Rules
     • Start Routines
     • End Routines
5. 5

   Reporting and Analysis

   3 topics

   • BEx Query Designer
     • Creating Queries
     • Filters and Variables
   • BEx Analyzer
     • Navigating BEx Analyzer
     • Using Excel Integration
   • BW Reporting Tools
     • BusinessObjects
     • Lumira
     • Analysis for Office
6. 6

   Administration and Performance

   3 topics

   • System Administration
     • User Management
     • System Monitoring
     • Backup and Recovery
   • Performance Optimization
     • Indexing and Aggregates
     • Compression Techniques
     • Partitioning
   • Data Archiving
     • Archiving Strategies
     • Implementing Archiving
7. 7

   Advanced Topics

   3 topics

   • SAP BW on HANA
     • Migration to HANA
     • Optimizing BW for HANA
   • SAP BW/4HANA
     • Differences from SAP BW
     • New Features
   • Integration with Big Data
     • Hadoop Integration
     • Spark Integration

SAP BW flashcards for SAP

23 of 51 cards from the SAP BW deck — real questions with worked answers.

1. What is SAP BW (Business Warehouse)?

   SAP BW is SAP's enterprise data warehousing solution used to collect, transform, store, and analyze data from multiple heterogeneous source systems for reporting and business intelligence.

2. What are the three core layers of the SAP BW data flow architecture?

   1) Extraction (staging/data acquisition from source systems), 2) Transformation/Storage (data warehouse and InfoProviders), and 3) Presentation/Reporting (BEx tools and analytics).

3. What is the LSA (Layered Scalable Architecture) in SAP BW?

   LSA is a reference architecture that organizes data into layers: Data Acquisition (PSA), Corporate Memory, Data Propagation, Business Transformation, Reporting, and Virtualization, ensuring scalable and maintainable EDW design.

4. What is the difference between SAP BW and SAP HANA?

   SAP BW is an application-layer data warehouse solution (modeling, ETL, reporting), while SAP HANA is an in-memory column-oriented database platform. BW can run on HANA (BW-on-HANA / BW/4HANA) to accelerate processing.

5. What is BW/4HANA?

   BW/4HANA is the next-generation data warehouse solution built exclusively to run on the SAP HANA in-memory database, using simplified objects (aDSO, CompositeProvider, Open ODS View) and Eclipse-based modeling.

6. What is an InfoObject in SAP BW?

   An InfoObject is the smallest reusable metadata building block in BW, representing business evaluation entities. Types include characteristics, key figures, units, time characteristics, and technical characteristics.

7. What are the two main types of InfoObjects?

   Characteristics (qualitative reference/attribute data such as customer, material, region) and Key Figures (quantitative values such as revenue, quantity, amount that are analyzed).

8. What are the four key-figure types available for InfoObject key figures in SAP BW?

   Amount (currency), Quantity (unit of measure), Number, Integer, Date, and Time. The main aggregatable numeric types are Amount, Quantity, Number, and Integer.

9. What is an attribute of a characteristic InfoObject?

   An attribute is an additional InfoObject that provides further information about a characteristic (e.g., 'city' as an attribute of 'customer'). Attributes can be display-only or navigational.

10. What is the difference between a display attribute and a navigational attribute?

    A display attribute is shown only as extra info and cannot be used to navigate/filter in a query, while a navigational attribute behaves like a characteristic and can be used for drill-down and filtering in reports.

11. What is an InfoProvider in SAP BW?

    An InfoProvider is any object that provides data for reporting/queries. It can physically store data (DSO, InfoCube, aDSO) or provide it virtually (CompositeProvider, MultiProvider, VirtualProvider).

12. What is an InfoCube in classic SAP BW?

    An InfoCube is a multidimensional InfoProvider based on the star schema, consisting of a central fact table linked to dimension tables containing characteristics, used for OLAP reporting.

13. What schema does a classic InfoCube use, and what makes it 'extended'?

    It uses the Extended Star Schema: the fact table connects to dimension tables, which reference SID (Surrogate ID) tables and master data tables, allowing shared master data and better performance than a classic star schema.

14. What is a DataStore Object (DSO) in SAP BW?

    A DSO is an InfoProvider that stores consolidated, cleansed transaction data at a detailed (document) level in flat transparent database tables, supporting overwrite functionality and delta handling.

15. What are the three tables of a standard DSO?

    1) Activation Queue (new/inbound data), 2) Active Data table (activated records used for reporting), and 3) Change Log (records of changes used for delta updates to targets).

16. What are the three types of classic DSOs in SAP BW?

    Standard DSO (with change log/activation), Write-Optimized DSO (fast load, no activation), and Direct-Update DSO (updated via APIs, no staging tables).

17. What is the key difference between a standard DSO and a write-optimized DSO?

    A standard DSO activates data and supports overwrite with a change log; a write-optimized DSO loads data directly into the active table without activation, offering faster loads but no overwrite/change log.

18. What is an Advanced DataStore Object (aDSO)?

    The aDSO is the central, unified persistence object in BW/4HANA and BW-on-HANA that replaces InfoCubes, DSOs, and PSA. Its behavior (staged, standard, data mart, etc.) is configured via modeling properties.

19. Which classic BW objects does the aDSO consolidate/replace?

    The aDSO replaces the classic InfoCube, standard DSO, write-optimized DSO, and PSA, providing a single flexible object whose behavior is defined by settings.

20. What are the three internal tables of a standard aDSO?

    Inbound table (Activation Queue, technical suffix 1), Active Data table (suffix 2), and Change Log table (suffix 3).

21. What is a Source System in SAP BW?

    A Source System is a system that supplies data to BW. Types include SAP systems (via extractors), non-SAP databases (DB Connect), flat files, web services, SAP HANA, ODP, and BI systems.

22. What is ODP (Operational Data Provisioning) in SAP BW?

    ODP is a framework for efficient data extraction and replication that supports delta handling via the Operational Delta Queue (ODQ), used as the recommended source-system connection in modern BW.

23. What is the difference between full extraction and delta extraction?

    Full extraction transfers the entire dataset each time, while delta extraction transfers only new or changed records since the last load, improving performance and reducing load volume.

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