If you are looking to enhance the report load performance of Power BI, irrespective of the complex data models and calculations involved, you can consider implementing the following solutions:
Optimize Data Model:
Remove Unused Columns and Tables: Delete every table or column that is not in use in any of your reports. This minimizes the data volume that Power BI needs to load.
Simplify Relationships: Complex relationships should be avoided as much as possible in the model. Where avoiding many-to-many relationships and circular relations is difficult, star schema design is recommended.
Use Aggregations: Rather than querying detailed data each time for every query, it is more sensible to pre-aggregate the data in the source or provide aggregation tables in Power BI for users to query. This is temporary as it reduces the amount of data being processed in real-time.
Optimize Calculations:
Minimize Complex DAX: Avoid complex DAX expressions with real-time concurrency on large datasets due to their high computational power requirements. Where applicable, do the calculations in Power Query or the source database and only bring the result.
Leverage Variables in DAX: It is essential to note that utilizing variables (VAR) within DAX formulations helps reduce most of the calculations, enhancing their effectiveness.
Optimize Time Intelligence Functions: Calculations that involve time tend to incur some costs. Thus, all the data tables used must be contiguous, have no gaps, and be properly connected to the relevant data. Employ less complex DAX, such as CALCULATE() with filters, instead of overly complicated nested IF statements.
Import Mode vs. Direct Query:
Use Import Mode Whenever It Is Feasible: Import mode is typically preferable because it usually takes less time and memory space since Power BI retrieves data into its memory storage, making it faster to run queries. Where there is a need for real-time data, direct query is advisable, but it tends to slow down performance because it sends every query to the database.
Employ Performance Analysis and Diagnostic Utilities:
The Performance Analyzer feature in Power BI can help identify which visualizations or queries take more time to load. DAX Studio will be useful in analyzing the queries to detect any likely causes of performance degradation so that DAX code and the data model can be enhanced.
The Query Diagnostics feature within Power BI Desktop will assist you in troubleshooting problems associated with query refresh latency, long-running queries, and delays due to network latency.
Simplify Visuals Where Possible:
Do not attempt to cram in so many complex visuals on one page. Each visual translates to a separate query, thus affecting the report's performance. Use drill-throughs, tooltips, or slicers where possible to reduce the number of visuals on every page.
Also, it is better to utilize slicers to filter out the data based on user needs rather than loading a single page with heavy visuals, as this helps reduce the loading time.
Reliable Data Loading and Refresh Mechanism:
If your data is large and frequently changing, to keep your dataset active, always use incremental data refresh to load only new or changed data. This prevents a large weight of data from being handled during data refresh.
Therefore, significant results are anticipated in the load times of the Power BI reports after adopting the above techniques, regardless of the dataset sizes and modeling complexities.
