Best Practice
- Staging the data is beneficial when you have data structures with a lot of complex joins, defines and selection criteria.
In those cases, running a job once to pre-process and create the final ‘flattened’ data structure saves on processing and makes content run faster.
Similar to RDBMS Materialized Views, the benefit in efficiency is derived by passing as much processing as possible to the RDBMS, incurring the cost once, and allowing users to run their reports and charts off of the staged data set. In this case, WebFOCUS doesn’t need to spend any time in resolving the joins and complex calculations and will simply return the values from a flat dataset.
- Another use case for staging the data is creation of ‘list of values’ (LOV) tables that can be used for dynamic parameter prompts.
For example, when you have a dynamic parameter that prompts for License Number, instead of selecting all unique values in the main table that hs 1M records in order to find 1000 License Numbers, you can point the prompt to a License Number LOV which contains the 1000 unique values. Staging these values allows you to speed up the prompt.
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