Thus, all performance improvements for Power BI concerning the SSAS Multidimensional Cube will include query execution improvements, connection settings, and aggregation methods. Let us consider some of the best practices for performance enhancement:
1. Optimize Connection Mode & Query Execution
Live Connection will free SSAS to do what it is designed to do.
This keeps query complexity down: filter by rows at the source and do not perform that filtering in Power BI.
Avoid calculated measure overloads in Power BI—better in SSAS with MDX calculations where they have superior performance.
2. Improve Aggregations & Partitioning
Aggregate in SSAS so that less is computed on the fly.
Custom aggregates will speed up the execution of the query by putting less data into memory.
Partition the cube optimally on the basis of the most frequently queried dimension(s) (eg. date).
3. Optimize DAX & MDX Queries
Simple DAX measures rather than longer computations, which depend on the processing ability of SSAS, are bestiary.
Where possible, avoid iterative ones (SUMX, FILTER); rather, use pre-aggregated cube measures.
Avoid giving additional details to an already-used query by minimizing the use of ALL or REMOVE FILTERS.
4. Optimize SSAS Processing & Caching
Ensure indexing and caching are properly set in SSAS, helping speed up the queries.
Proactive caching should be used to refresh the pre-aggregated results, avoiding genuine real-time queries.
MDX's execution plans should be monitored and optimized by using the SSAS Profiler or Performance Monitor.
5. Reduce Unnecessary Data Transfers
Avoid bringing all cube attributes into Power BI—only select needed fields.
Use hierarchies and key attributes in SSAS instead of flattening dimensions in Power BI.
Minimize cross-filtering relationships between SSAS dimensions.
