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What is incremental load in Power BI and what are the benefits of using it?

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Incremental load is a methodology of refreshing data in a dataset where only the new and updated data are extracted in the place of a full refresh of the entire dataset.
Through incremental refresh, you can make performance gains and reduce resource consumption for datasets that have high rates of data change. This goes beyond the capabilities of the traditional refresh task by placing into the system automated partitioning and management for tables holding such business-critical constantly changing data.

Supported data sources: Incremental refresh and real-time data work best with structured, relational data sources but can be applied to any data source. The requirement is that the data source should be date-filterable.

In this process, the Power BI service partitions the table and loads only those rows associated with the newest data into the model. It can create, on-the-fly, seen-only incremental refresh partitions and seen-only historical partitions for previously loaded data. DirectQuery can also support real-time data via its own DirectQuery partition.

Benefits:

  • Faster refreshes
  • More reliable refreshments.
  • Reduced resource consumption
  • Ability to handle large datasets.

Configuration: To set up incremental refresh, it must be done in Power BI Desktop. The following steps outline the process:

  1. Create two Power Query date/time parameters named RangeStart and RangeEnd (case-sensitive).
  2. Apply custom date filters to your table’s date column by using these parameters.
  3. Define an incremental refresh policy for the table.
  4. Set the Archive Data Starting option (in Days/Months/Quarters/Years) to specify how far back the data should be archived.
  5. Set the Incremental Refresh Starting option (in Days/Months/Quarters/Years) to determine the period from which data will be incrementally refreshed.
  6. Adjust other optional settings as needed.

Once the model is published to the Power BI service, the defined policy is applied by the service to create and manage table partitions. Additionally, the policy is utilized during refresh operations.

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