Question

How do I reduce the impact of large cardinality fields on performance in Power BI?
I am working on a Power BI model that includes fields with high cardinality, which is negatively impacting performance. What strategies can I use to reduce the performance impact of these fields, including options like summarizing data, removing unnecessary columns, or using optimized DAX functions?

Answer 1

Measures to mitigate the extent of performance degradation associated with large cardinality fields in Power BI include the following.

Summarize Your Data and Reduce the Granularity: Data can be pre-aggregated in the source application or Power Query before being loaded into Power BI. Reducing the granularity of the data, for example, by aggregating transactions to a daily or weekly basis, can significantly reduce cardinality.

Adhere to Proper Data Modeling Techniques:

Remove unwanted columns and tables to reduce the model size.

Surrogate keys or grouping categories might be employed instead of using specific fields (e.g., transaction IDs).

Unique values are stored in dimension tables separate from the fact tables.

DAX Optimization and Indexing:

Avoid visualization or slicer use of high-cardinality columns; instead, use summarized fields or dimension tables.

Utilize optimized DAX Functions like SUMX and CALCULATE on pre-aggregated data.

Efficient filtering via TOPN or RANKX to restrain data.