Power BI reports featuring complex DAX calculations can also be optimized by working on the DAX expressions and the filters and by lessening the data that the model has to carry around. This is a short one:
1. DAX Calculations Improvement
- Remove Unwarranted Calculated Columns: Calculated columns take more memory; do these in Power Query instead, where values are not expected to change dynamically.
- Reduce the Application of Iterators (for example, SUMX, AVERAGEX): Iterators perform their operations on a row-by-row basis, which can be too long at times, especially on large data sets. Where possible, use aggregating functions like SUM or AVERAGE instead.
- Purge Nested Calculations: Nested calculations with multiple levels should not be used; they should be divided into separate measures that can be better managed and performed and then Combined.
2. Filter Context and Relationships Optimization
- Make CALCULATE Filters Less Detailed: The more complex the filters in CALCULATE are, the more time it takes to execute the queries, which is undesirable. Do not use filters that are not necessary, and ensure that every filter provides some realistic benefit.
- Avoid Bi-Directional Relationships Where Possible: Bi-directional filters make dependencies more complex, which slows down reports when they are viewed. Do this only if it is necessary to define relationships in the model.
- Pre-Aggregate Data: Pre-aggregation of data in Power Query or SQL helps reduce the amount of work that DAX has to do in real-time on large amounts of high-cardinality data sets.
3. Reduce Visual Clutter
- Minimize the Use of Complex Products: Using complex measures across several visuals increases the processing burden. Limit these to recurrently used pages or complex visuals with fewer calculations.
- Do not overuse conditional formatting: Conditional formatting in visuals, especially for widely used visuals, can prove costly. It should be used efficiently and, therefore, only directed towards crucial metrics.
4. Consider Using Aggregated and Summarized Grids
- Design Basic Tables: Basic tables for the values’ actors that are checked on regularly help eliminate extensive efforts in heavy calculations. These tables serve to ease quick calculations and, hence, reduce the number of in-report calculations.
- Adjust the Data Granularity: If there is a way to achieve that, less detail should be applied to the data model because smaller models have faster computational power as they draw less data.
As a result, effective DAX can be performed while avoiding performance issues such as latency, allowing users to have seamless experience management.
