Forum Discussion
Helper IVIncremental data load in Power BI file
Hi Marico ,
Yes, you can improve report performance even with datasets larger than 5 GB. Power BI offers several methods to help optimize speed and user experience. Here are some tips:
- Use Aggregations: Create summary tables to reduce data volume and improve load times. Power BI can switch between aggregated and detailed data as needed.
- Optimize the Data Model: Remove unused columns and tables, avoid high-cardinality columns, and use numeric keys for relationships.
- Use Hybrid or Composite Models: Store large fact tables in DirectQuery mode and import only smaller dimension tables to save memory.
- Apply Incremental Refresh: Use incremental refresh for large datasets with date/time fields to process only recent or changed data.
- Limit Visual and Filter Interactions: Reduce the number of slicers and filters, and use the "Apply" button for filters to control when visuals update.
- Split Reports: Divide complex reports into multiple pages or files so users only load what they need.
- Use Performance Analyzer: Identify slow visuals or queries with the Performance Analyzer and optimize them.
- Upgrade to Power BI Premium: Premium capacity provides more memory and processing power for large datasets.
Thank you again.
Helper IVAnonymous Thanks for sharing this.
However, my problem is different. My concern is regarding the incremental refresh will help to refresh the data quickly. However, it won't help to access the report quickly when client applying the filter on the report.
At the moment, even one filter applied on the report page takes 5 mins to bring the data to screen. Report is super slow right now in service.
I am currently using import mode. Will direct mode or any other way help the report accessibility quickly?
Hi Marico ,
yes, once you’ve set up the Range Start and Range End parameters in Power Query and applied them to a date column, the Incremental Refresh works exactly as intended in Power BI Service. When you first publish the report, it performs a full refresh to load all historical data and establish the required partitions. After that, during scheduled or manual refreshes, it only processes the latest data based on the policy you've defined (like the last 7 days), rather than reloading the entire dataset again. This improves performance and efficiency. If you're noticing a full dataset refresh every time, it’s worth checking if the parameters are properly applied in your date filter, and whether the dataset was refreshed at least once post-publish to create those partitions. I’ve also shared Microsoft documentation and screenshots earlier to support this setup.
Thank you .
- Marico
Anonymous Yes, I understand that enabling incremental refresh improves data load and refresh time.
My ask -
How to speed up report performance or filtering for end users, when Power BI dataset is greater than 5 GB?
Hi Marico ,
Yes, you can improve report performance even with datasets larger than 5 GB. Power BI offers several methods to help optimize speed and user experience. Here are some tips:
- Use Aggregations: Create summary tables to reduce data volume and improve load times. Power BI can switch between aggregated and detailed data as needed.
- Optimize the Data Model: Remove unused columns and tables, avoid high-cardinality columns, and use numeric keys for relationships.
- Use Hybrid or Composite Models: Store large fact tables in DirectQuery mode and import only smaller dimension tables to save memory.
- Apply Incremental Refresh: Use incremental refresh for large datasets with date/time fields to process only recent or changed data.
- Limit Visual and Filter Interactions: Reduce the number of slicers and filters, and use the "Apply" button for filters to control when visuals update.
- Split Reports: Divide complex reports into multiple pages or files so users only load what they need.
- Use Performance Analyzer: Identify slow visuals or queries with the Performance Analyzer and optimize them.
- Upgrade to Power BI Premium: Premium capacity provides more memory and processing power for large datasets.
Thank you again.
Hi Marico ,
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.
Thank you.