Question

How can you efficiently manage Power BI’s incremental refresh for very large tables, such as sales or transactional data?

My Power BI project involves working with very large tables, such as sales or transactional data, which take a long time to refresh. I’ve heard that incremental refresh can optimize this process by only refreshing the most recent data. How can I set up and manage incremental refresh efficiently for these types of tables while ensuring the integrity of the data?

Answer 1

Sustainable incremental refresh management entails the following efficient steps and techniques in Power BI for very large tables:

Enable Incremental Refresh: In Power BI Desktop, go into model view, open the table you wish to optimize, and then define the date or time range parameters such as RangeStart and RangeEnd with the Manage Parameters option. Such parameters will set the range of data that could be available for incremental refresh.

Set Incremental Refresh Policy: The parameters are then configured within the filter of the table in Power Query. The date column is right clicked and has a date filter condition added to it that further employs RangeStart and RangeEnd to define the period for filtering. Then, in the model view, right-click the table, select Incremental Refresh, and configure settings as the archive period and refresh frequency. 

Publish and Test: After setting up the configuration, publish the report to Power BI Service. Now, test incremental refresh by refreshing that dataset. The expected result should be that only the newly added or changed data will be updated while archive data are retained. Improve the performance and check the accuracy of the data after refresh.