Here's an article titled "Developing Custom Data Sources in SAP Business Intelligence (SAP BI)" suitable for the subject of SAP-BI in the SAP field:
In the realm of enterprise data management, SAP Business Intelligence (SAP BI) plays a vital role in transforming raw data into meaningful insights. At the heart of SAP BI lies the ability to source data effectively from diverse systems. While SAP provides a rich set of standard data sources, many business scenarios demand custom data sources to address unique organizational needs. This article explores the concept, development, and best practices of creating custom data sources in SAP BI.
¶ Understanding Data Sources in SAP BI
A data source in SAP BI refers to the origin of data that is used in a data extraction process. Standard SAP systems (like SAP ECC, SAP S/4HANA) come with pre-delivered Business Content data sources, which are ready-made extractors designed for common business scenarios.
However, not all business processes can be captured through standard extractors. In such cases, custom data sources must be created to extract data from:
- Custom Z-tables
- Non-SAP systems
- Extended or enhanced standard tables
- Flat files or web services
Developing a custom data source becomes necessary when:
- Standard data sources do not fulfill specific business requirements.
- Custom business logic is embedded in Z-tables or customer-specific developments.
- Data resides in external systems without standard connectors.
- There is a need to enrich or transform data before loading into BI.
Start by understanding the data extraction need:
- Which data is required?
- Where does it reside?
- How often should it be extracted?
- What is the data volume and latency requirement?
Define the structure of the custom data source:
- Identify fields to be extracted.
- Determine key fields and data types.
- Decide on delta vs. full extraction mode.
Use Transaction Code: RSA1 (in BI) or RSA6/RSA5 (in source system):
- Use Transaction Code: RSO2 to create a generic data source.
- Choose appropriate extractor types: Table/View, Function Module, or InfoSet.
- For complex logic, use a Function Module-based Extractor.
Enhance the logic for:
- Data filtering and transformation
- Delta management (timestamp, numeric pointers)
- Error handling
¶ 5. Transport and Replicate the Data Source
Once the data source is created and tested:
- Transport it to the relevant environments (QA/Prod).
- In SAP BI, replicate the data source from the source system using RSA1.
- Define InfoObjects for key figures and characteristics.
- Create DataStore Objects (DSO) or InfoCubes to hold data.
- Define Transformation Rules and Data Transfer Processes (DTPs).
¶ 7. Test and Validate
- Run test extractions using RSA3 in the source system.
- Validate the data in SAP BI.
- Monitor performance and data accuracy.
- Reuse Standard Logic: If possible, enhance existing extractors instead of building new ones.
- Efficient Delta Mechanism: Use timestamps or change pointers for incremental loads.
- Modular Design: Use function modules or CDS views for complex logic.
- Governance: Document and version-control your custom data source.
- Performance Tuning: Use indexes and buffer optimizations on the source tables.
- RSA5 / RSA6 / RSO2 – Extractor maintenance
- SE11 / SE80 – Data Dictionary and Function Module development
- CDS Views / AMDP (ABAP Managed DB Procedures) – Modern data modeling (S/4HANA)
- SAP BW Modeling Tools (Eclipse) – Data modeling on BW/4HANA
Developing custom data sources is a crucial skill in the SAP BI toolkit. It enables organizations to tap into the full potential of their enterprise data, ensuring that decision-makers have access to tailored, reliable, and timely information. With careful planning, sound technical design, and adherence to best practices, custom data sources can significantly enhance the effectiveness of a BI solution in a SAP environment.