SAP BW/4HANA is a next-generation data warehousing solution that fully leverages the power of the SAP HANA in-memory database. Unlike traditional relational databases, HANA offers real-time processing, advanced analytics, and a simplified data model through its columnar storage and parallel processing capabilities.
To maximize the benefits of SAP BW/4HANA, data modeling must be optimized for HANA. This article outlines best practices for HANA-optimized data modeling to help SAP professionals design high-performance, scalable, and agile data warehousing solutions.
Traditional BW modeling techniques focused heavily on data redundancy, complex transformations, and multi-layered InfoProviders to achieve performance. With BW/4HANA, these legacy approaches must evolve to harness HANA’s capabilities—reducing complexity and data duplication while improving query speed and flexibility.
- Favor Advanced DSOs Over InfoCubes: InfoCubes are legacy objects optimized for disk-based databases. Advanced DSOs (ADSO) are the primary persistent object in BW/4HANA and are designed to leverage HANA’s columnar store.
- Use CompositeProviders for Logical Joins: Instead of physically joining data in multiple layers, use CompositeProviders to virtually combine different InfoProviders for reporting.
- Use Open ODS Views to integrate external data sources virtually without replication.
- This reduces data redundancy and accelerates time-to-insight by allowing real-time access to data from S/4HANA, cloud platforms, or third-party systems.
- When complex logic or advanced calculations are needed, use native HANA modeling objects such as Calculation Views or CDS Views.
- BW/4HANA can consume these views as data sources, combining BW’s semantic layer with HANA’s advanced capabilities.
¶ 4. Avoid Unnecessary Aggregates and Indexes
- HANA’s in-memory and columnar engine reduces the need for pre-calculated aggregates.
- Instead of creating multiple aggregates, rely on BW/4HANA’s optimized compression and HANA’s query processing.
- Use indexes judiciously only when analytics or query patterns demand.
- Use numeric key figures with appropriate data types (e.g., decimals, currency, or quantity) for efficiency.
- Limit the number of characteristics in fact tables to essential dimensions to reduce join complexity.
- Favor flat master data structures or attribute views over complex hierarchies for performance.
- Partition large ADSOs based on meaningful business criteria such as fiscal year, region, or product category.
- Partitioning improves query performance by enabling partition pruning and parallel processing.
¶ 7. Use HANA Smart Data Access (SDA) and Smart Data Integration (SDI)
- SDA and SDI allow seamless integration with remote or cloud data sources.
- Use these tools to create virtual tables or replicate data in real-time, maintaining agility without sacrificing performance.
¶ 8. Implement Delta and Real-Time Data Loads
- Use delta mechanisms to minimize data load times.
- Leverage real-time data acquisition for operational reporting scenarios using advanced DSOs and Data Transfer Processes (DTPs).
- Limit the use of calculated key figures and complex exceptions within queries.
- Use BW/4HANA Query Designer or SAP Analytics Cloud to test query performance.
- Cache frequently accessed data at the presentation layer when needed.
¶ Monitoring and Continuous Improvement
- Use SAP BW/4HANA Administration Cockpit and SAP HANA Studio to monitor query performance and resource utilization.
- Analyze execution plans and identify bottlenecks.
- Continuously refine data models based on usage patterns and business changes.
Adopting HANA-optimized data modeling practices is essential for unlocking the full potential of SAP BW/4HANA. By simplifying data structures, leveraging virtualization, using native HANA capabilities, and focusing on performance, organizations can build scalable, efficient, and agile data warehousing solutions.
Following these best practices empowers businesses to achieve faster time-to-insight, reduce costs, and support advanced analytics initiatives in the digital age.
Author: [Your Name]
Date: May 2025
Category: SAP BW/4HANA – Data Modeling