1 ACCEPTED SOLUTION
When dealing with massive datasets in Power BI that are too large for Import mode and perform poorly or are restricted in DirectQuery, here are effective alternatives you can consider:
✅1. Use Incremental Refresh (Premium or PPU)
Allows you to import only new or changed data during refreshes, significantly reducing load.
- Requires a RangeStart and RangeEnd parameter in Power Query.
- Setup is done in Power BI Desktop, deployment requires Power BI Service (Premium workspace).
- Official Guide: Incremental Refresh
✅2. Use Aggregations with DirectQuery
Model aggregated tables in Import mode and keep detail-level data in DirectQuery. This boosts performance for most queries.
- Design summary tables (e.g., by month, region) and map them as aggregations in the model.
- Queries hit Import mode unless user drills to raw data.
✅3. Implement Query Folding + Filtering via UI
Design filters in the report UI that reduce the result set before loading into Power BI. This avoids the limitations of parameters that users can’t change dynamically.
- Create a Date table or Category filter in the model.
- Ensure filters are applied early in Power Query to benefit from query folding.
- This is best paired with DirectQuery or Incremental Load.
✅4. Paginated Reports
If you're on Power BI Report Server or Premium, use Paginated Reports for detail-heavy reports that need to hit large SQL data sources directly.
✅5. Preprocess Data Outside Power BI
Consider ETL solutions like:
- SQL Server Agent Jobs for preprocessing and storing summaries in separate tables.
- Azure Data Factory or Synapse pipelines for filtering and aggregating large datasets.
🔄Workaround for Parameter Filtering via UI
Power BI parameters are static, but a workaround is to create slicers that dynamically control filters through DAX measures or query filters. Avoid using parameters for user-driven filters in reports.
Try designing a Date or Category slicer and linking it to your queries or measures instead of relying on Power BI parameters.
✔️If my message helped solve your issue, please mark it as Resolved! 👍If it was helpful, consider giving it a Kudos! |
Hi @homelander123 ,
Thanks for reaching out to the Microsoft fabric community forum.
I would also take a moment to thank @SolomonovAnton , for actively participating in the community forum and for the solutions you’ve been sharing in the community forum. Your contributions make a real difference.
May I ask if you have resolved this issue? If so, please mark the helpful reply and accept it as the solution. This will be helpful for other community members who have similar problems to solve it faster.
Best Regards,
Menaka.
Community Support Team
Hi @homelander123 ,
I wanted to check if you had the opportunity to review the information provided. Please feel free to contact us if you have any further questions. If the response has addressed your query, please accept it as a solution so that other community members can find it easily.
Best Regards,
Menaka.
Community Support Team
Hi @homelander123 ,
As we haven’t heard back from you, so just following up to our previous message. I'd like to confirm if you've successfully resolved this issue or if you need further help. If yes, you are welcome to share your workaround and mark it as a solution so that other users can benefit as well. If you find a reply particularly helpful to you, you can also mark it as a solution.
If you still have any questions or need more support, please feel free to let us know. We are more than happy to continue to help you.
Thank you for your patience and look forward to hearing from you.
When dealing with massive datasets in Power BI that are too large for Import mode and perform poorly or are restricted in DirectQuery, here are effective alternatives you can consider:
✅1. Use Incremental Refresh (Premium or PPU)
Allows you to import only new or changed data during refreshes, significantly reducing load.
- Requires a RangeStart and RangeEnd parameter in Power Query.
- Setup is done in Power BI Desktop, deployment requires Power BI Service (Premium workspace).
- Official Guide: Incremental Refresh
✅2. Use Aggregations with DirectQuery
Model aggregated tables in Import mode and keep detail-level data in DirectQuery. This boosts performance for most queries.
- Design summary tables (e.g., by month, region) and map them as aggregations in the model.
- Queries hit Import mode unless user drills to raw data.
✅3. Implement Query Folding + Filtering via UI
Design filters in the report UI that reduce the result set before loading into Power BI. This avoids the limitations of parameters that users can’t change dynamically.
- Create a Date table or Category filter in the model.
- Ensure filters are applied early in Power Query to benefit from query folding.
- This is best paired with DirectQuery or Incremental Load.
✅4. Paginated Reports
If you're on Power BI Report Server or Premium, use Paginated Reports for detail-heavy reports that need to hit large SQL data sources directly.
✅5. Preprocess Data Outside Power BI
Consider ETL solutions like:
- SQL Server Agent Jobs for preprocessing and storing summaries in separate tables.
- Azure Data Factory or Synapse pipelines for filtering and aggregating large datasets.
🔄Workaround for Parameter Filtering via UI
Power BI parameters are static, but a workaround is to create slicers that dynamically control filters through DAX measures or query filters. Avoid using parameters for user-driven filters in reports.
Try designing a Date or Category slicer and linking it to your queries or measures instead of relying on Power BI parameters.
✔️If my message helped solve your issue, please mark it as Resolved! 👍If it was helpful, consider giving it a Kudos! |
