Data modeling is a crucial aspect of working with SAP HANA, the cutting-edge in-memory database platform. Effective data models ensure that data is stored efficiently, queries perform well, and business intelligence processes run smoothly. Because SAP HANA combines transactional and analytical processing in a single system, it requires a different approach to data modeling compared to traditional databases.
This article covers the best practices for data modeling in SAP HANA, aimed at SAP professionals seeking to leverage the full power of this platform.
¶ Understanding Data Modeling in SAP HANA
SAP HANA supports multiple data modeling techniques, including:
- Attribute Views (deprecated in newer versions but still in use)
- Analytic Views (also being phased out)
- Calculation Views (the recommended approach)
- Graph and Spatial models
The focus now is on Calculation Views, which combine multiple data sources and business logic into reusable virtual models.
¶ 1. Use Calculation Views Instead of Attribute and Analytic Views
- Calculation Views provide the most flexibility, combining data from multiple tables and supporting complex logic such as filters, unions, aggregations, and joins.
- Avoid using deprecated Attribute and Analytic Views to ensure future compatibility and better performance.
¶ 2. Favor Columnar Storage for Tables
- SAP HANA stores data in columnar format by default, which accelerates read and analytic operations.
- Design your models to leverage columnar storage and compression for performance gains.
- Break complex models into smaller, reusable calculation views.
- Simplify joins and filters to minimize query complexity.
- Avoid unnecessary layers in the model, which can add overhead.
- Prefer inner joins over outer joins when possible, as outer joins can degrade performance.
- Minimize the use of cross joins unless absolutely necessary.
¶ 5. Push Down Filters and Aggregations
- Use filters early in the data flow to reduce the amount of data processed.
- Aggregate data as close to the source as possible to improve query speed.
¶ 6. Define Clear and Meaningful Naming Conventions
- Use consistent naming for views, tables, and columns.
- This improves maintainability and collaboration across development teams.
¶ 7. Avoid Using Calculated Columns in Large Datasets When Possible
- Calculated columns are evaluated at runtime, which can slow down performance on large data sets.
- Instead, pre-calculate values during ETL or in database tables if possible.
- Make your calculation views dynamic and flexible by using input parameters.
- This reduces the need to create multiple views for similar requirements.
- Use appropriate data types to minimize storage and improve query efficiency.
- For example, use
INTEGER instead of VARCHAR for numeric IDs.
- Use SAP HANA’s built-in Plan Visualizer (PlanViz) tool to analyze and optimize execution plans.
- Regularly monitor models for performance bottlenecks and adjust accordingly.
- Model for Business Use Cases: Always keep business requirements in mind; tailor data models to fit analytical needs.
- Reuse Existing Models: Leverage existing calculation views instead of duplicating logic.
- Security and Authorization: Implement data-level security within calculation views to restrict sensitive data access.
Effective data modeling in SAP HANA is a blend of art and science, balancing technical best practices with business needs. By following these best practices, SAP professionals can design efficient, scalable, and maintainable data models that unlock the true power of SAP HANA’s in-memory computing.
Mastering these principles will not only improve query performance but also streamline reporting and analytics, driving better business outcomes.