Efficient and effective data storage is a foundational element of successful enterprise data warehousing. With SAP BW/4HANA, SAP revolutionizes data storage by harnessing the power of the SAP HANA in-memory database, offering superior performance, scalability, and flexibility. However, to fully exploit these capabilities, organizations must adopt best practices tailored to SAP BW/4HANA’s architecture and data modeling paradigms. This article outlines the essential best practices for data storage in SAP BW/4HANA.
¶ Understanding SAP BW/4HANA Data Storage Concepts
SAP BW/4HANA is designed around simplified data models and leverages SAP HANA’s in-memory columnar storage. Key data objects include:
- Advanced DataStore Objects (aDSOs) — central to storing detailed transactional and master data.
- CompositeProviders — for federated access and combining multiple data sources.
- Open ODS Views — enabling virtualization and integration of external data sources without replication.
Unlike traditional BW, SAP BW/4HANA minimizes aggregates and relies heavily on runtime calculations, reducing storage overhead.
- Choose the Correct aDSO Type: BW/4HANA offers different aDSO types—standard, direct update, and write-optimized—each suitable for specific use cases. Write-optimized aDSOs are ideal for staging and initial data load, while standard aDSOs serve as consolidated, cleaned data stores.
- Keep Data Granular: Store data at the lowest required granularity to support detailed reporting but avoid unnecessary detail that increases storage and processing time.
- Avoid Excessive Data Duplication: Utilize CompositeProviders and Open ODS Views to combine data virtually instead of duplicating it.
- Hot, Warm, and Cold Data Storage: Classify data based on access frequency and business relevance. Keep hot data in SAP HANA’s in-memory storage for fast access, while moving warm and cold data to cost-efficient storage layers such as SAP IQ or Hadoop using data tiering techniques.
- Leverage SAP HANA Native Data Tiering: BW/4HANA supports native integration with SAP HANA’s Data Tiering Optimization (DTO), enabling seamless movement between memory and disk-based storage.
¶ 3. Optimize Data Compression and Partitioning
- Use SAP HANA’s Compression: Take advantage of HANA’s advanced compression algorithms to reduce storage footprint.
- Partition Large Tables: For large aDSOs, partition tables based on time or other relevant keys to improve query and load performance.
- Open ODS Views: Use Open ODS Views to integrate external data sources without physically replicating data, reducing storage requirements and improving data freshness.
- Smart Data Access (SDA): Connect to external databases like Hadoop or cloud storage virtually, allowing BW queries to fetch external big data in real time.
¶ 5. Regular Data Archiving and Housekeeping
- Implement data aging and archiving strategies to remove obsolete or infrequently used data from primary storage.
- Clean up unused or outdated objects to maintain a lean data warehouse environment.
- Use SAP BW/4HANA and SAP HANA monitoring tools to track storage consumption and query performance.
- Analyze data load and query patterns to identify optimization opportunities.
- Overloading aDSOs with unrelated data can cause performance degradation.
- Ignoring data tiering opportunities leads to unnecessary high memory usage and costs.
- Excessive replication of data across objects can increase maintenance complexity.
Data storage in SAP BW/4HANA demands a strategic approach that balances performance, cost, and flexibility. By designing purpose-driven aDSOs, leveraging data tiering, embracing virtualization, and continuously monitoring storage health, organizations can build scalable and efficient data warehouses that harness the full power of SAP HANA. Following these best practices not only optimizes storage but also enhances overall data processing and reporting capabilities, enabling faster and smarter business decisions.