Handling Big Data without Inhibiting the Reports in Power BI-Some Solutions:
Use Data Model Optimization: To reduce complexity and improve query performance, your data must be arranged in a star schema rather than a snowflake schema.
Use Aggregations: Pre-aggregated data: To reduce the volume, summarize tables or data at a higher level, like daily or monthly totals, prior to loading into Power BI.
Using aggregated tables: use the aggregated tables for summarized data, and let Power BI automatically switch to detailed data only when needed.
Make Use of Query Reduction Techniques: Push to Source: Wherever possible, use DirectQuery or push queries to the database layer, letting the database do the heavy computation.
Incremental Refresh: Implement incremental refresh for large data sets in such a way that only new or changed data is loaded. This improves the refresh times of reports and dashboards.
Optimize DAX Calculations: Keep your DAX measures simple and efficient. Avoid row-by-row calculations like EARLIER or FILTER; built-in aggregations can be used.
Avoid Complex Nested Calculations: Avoid using complex or deeply nested DAX formulas, which slow down query processing.
Use VertiPaq Analyzer
The first step is to utilize the memory usage of your Power BI model with area for optimization (like removing high cardinality columns or compressing data).
Reduce Visual Complexity: Limit the number of visuals on a single report page and use lightweight visuals that consume fewer resources. Each visual queries the dataset, so the fewer the number, the better the performance.
Use Efficient Storage Modes: The import mode supports smaller datasets but also supports better performance. DirectQuery suits the handling of larger datasets, though with some limitations regarding interactivity and speed.
Optimizing the Data Model, Reducing Calculations, Using Aggregations, and Reducing Unnecessary Data can easily be processed without affecting the performance of the Power BI reports.
