Siebel Business Analytics Server Administration Guide > Completing Setup and Managing Repository Files > Process of Completing the Setup for a Repository File >
Test and Refine the Repository
When the repository is created and you can connect to it, run sample queries against it to test that it is created properly. Correct any problems you find and test again, repeating this process until you are satisfied with the results.
Tips For Performance Tuning
The Physical Data Model should more closely resemble the Siebel Business Analytics metadata model (for example the star schema) instead of an transactional database system (approximately 3NF). If the Physical model is set up like the underlying transactional model, performance problems and configuration problems could likely arise. For additional tips, see Guidelines For Designing a Repository.
NOTE: Make sure the metadata is generating the correct record set first, then focus on performance tuning activities (such as adding sources).
- Accuracy of metadata is more important than improved performance.
- In general, push as much processing to the database as possible. This includes tasks such as filtering, string manipulation, and additive measures.
- Move as much of the query logic to the ETL as possible to improve system response time. Pre-calculation of additive metrics and attributes will reduce query complexity and therefore response time.
- Use base and extension tables to allow for a cleaner upgrade path. To improve runtime performance, merge the two tables into a third one, which is then mapped into Siebel Business Analytics. Although this technique requires more effort and a larger ETL batch window, it insulates the system from upgrade errors while still providing optimal performance.
- Denormalize data into _DX tables using the ETL process to reduce runtime joins to other tables.