Managing and Exporting High-Volume Datasets (~1M Records) in Power BI

Hi Anonymous ,

Thank you for reaching out to the Microsoft Community Forum.

Please try below things.

1. Use a centralized DAX template in Power Automate and dynamically inject field names using Compose actions. Consider using UNION or ADDCOLUMNS to merge multiple field parameter outputs if they belong to different tables. Use calculated tables in Power BI to pre-shape data based on slicer selections and field parameters, reducing complexity in the export logic.

2. Use DAX measures or calculated columns to capture slicer values and expose them in a table visual. In Power Automate, use “Export data” from a visual that includes these slicer-bound values. Alternatively, use Power BI REST API to run DAX queries with parameters passed from slicers.

3. Exporting Large Volumes , Paginate the export using TOPN and SKIP logic in DAX, and loop through pages in Power Automate. Use Azure Data Lake or Synapse as a staging area if you are on premium capacity. Consider DirectQuery or Hybrid Tables to offload data processing to the source system.

4. Use Performance Analyzer to identify bottlenecks and optimize visuals. Avoid using too many visuals with dynamic fields; instead, use tables with conditional formatting. Use aggregated tables or summary views for initial filtering before exporting detailed data.

5. Paginated Reports handle large exports better. You can embed them in Power BI and pass parameters from slicers.

6. Use Power Automate to trigger an Azure Function that runs a query and writes to blob storage or SQL.

7. Use Power BI Dataflows to pre-process and store filtered data, then export from there.

I hope this information helps. Please do let us know if you have any further queries.

Regards,

Dinesh