Question

What strategies can I use to optimize Power BI datasets for incremental refresh?
Incremental refresh is an essential technique for improving the performance of large datasets in Power BI. This question aims to explore strategies for configuring incremental refresh efficiently, ensuring that only new or updated data is loaded into the model, thereby optimizing dataset refresh times and minimizing resource consumption.

Answer 1

To optimize Power BI datasets for incremental refresh, follow these effective strategies:

1. Configure Range Parameters: Set up two parameters, RangeStart and RangeEnd, in Power BI Desktop to define the refresh period. These parameters filter the dataset to load only new or updated data, reducing the volume processed during refreshes.

2. Use Proper Date/Time Columns: Ensure the dataset contains a date/time column suitable for incremental refresh. The column must be contiguous, accurate, and indexed in the source system. Avoid calculated columns, as they can hinder query folding and prevent efficient filtering.

3. Enable Query Folding: Query folding ensures that transformations are pushed back to the data source, reducing the amount of data retrieved. Use DirectQuery or supported data sources that allow folding. Avoid complex transformations that prevent query folding, such as using Power Query functions that manipulate rows individually.

4. Set Up Refresh Policies: In the incremental refresh settings, specify the range to archive (e.g., keep last 5 years) and the refresh period (e.g., refresh last 1 month). This ensures only the most recent data is updated while historical data remains static, optimizing refresh time and resource consumption.

5. Test and Monitor Performance: Validate incremental refresh by publishing the report to the Power BI service and triggering a refresh. Monitor refresh times and resource usage. Adjust the refresh settings if needed to balance performance and accuracy.