These are the tools you would use to optimize your DAX applications and decrease query execution times in Power BI.
First and foremost:
1. Reduce Context Transitions
- Use Measures Not Calculated Columns- Measures are much better suited for handling this because they do not have actual columns; rather, they will update their values only when they are calculated in context. Avoid Unnecessary CALCULATE() Calls CALCULATE incurs a context switch and should be taken only when necessary. Reduce Nested Filters-Filter before CALCULATE or, failing that, use simpler filter logic instead of filtering within CALCULATE.
2. How To Optimize Your Iterative Functions (SUMX, AVERAGEX, etc.)
- Aggregated Tables: Do not iteratively create large tables; create them pre-aggregated. Use Variables (VAR)-Store the intermediate calculation in some variable so that it will not be recalculated within the row context repeatedly. Reduce Row Iterations-expressed as SUMX (Table, Expression), and work with SUM (column) when you can because scalar aggregations run faster.
3. Improve Data Model & Query Execution
- Optimize Relationships: Every relationship you have in your model should be 1-to-many and never many-to-many; the latter slows down your queries. Reduce Cardinality-high-cardinality columns are what slow DAX evaluation; hey, don't use text columns-just create some numeric keys. Use Performance Analyzer & DAX Studio: Find slow measures and identify their execution times with Query Plan & Server Timings.
