When implementing incremental data refresh in Power BI, my main objective is to balance performance and efficiency — especially for large datasets where refreshing the entire model daily is time-consuming and resource-heavy. Incremental refresh allows Power BI to load only new or updated data instead of reprocessing the entire dataset each time.
I usually begin by identifying the table that holds historical data, typically a fact table such as Sales or Transactions. The first step is to make sure the source table includes a date column that can be used to filter data based on time — for example, an OrderDate or ModifiedDate column. Then, in Power Query, I create two range parameters: RangeStart and RangeEnd, both of type DateTime. These parameters act as placeholders for Power BI to determine which portion of data to load during each refresh cycle.
Next, I apply these parameters in the query filter — something like:= Table.SelectRows(Source, each [OrderDate] >= RangeStart and [OrderDate] < RangeEnd)
This ensures that during dataset refresh, Power BI dynamically replaces these parameter values to pull only the relevant partitions of data. Once the parameters are applied, I publish the report to the Power BI Service.
In the Power BI Service, I enable Incremental Refresh from the dataset settings. For example, I might configure it to “store data for the past five years” but “refresh only the last one month.” This setup means Power BI will keep five years of data in the model, but during each refresh, it only queries and updates the past month, leaving the historical partitions untouched.
I applied this approach in a retail analytics project where the sales table contained over 250 million records. Before using incremental refresh, the dataset refresh took nearly two hours. After enabling incremental refresh, the refresh time dropped to around 10 minutes because only the recent data (typically the last few days) was being reloaded.
One of the challenges I encountered was data source compatibility. Incremental refresh requires the data source to support query folding — meaning Power BI must be able to push the date filter logic back to the database. Initially, our data source was a custom API that didn’t support folding, so Power BI ended up loading all data locally before filtering. To fix this, I switched to using a SQL view that exposed the same API data via an intermediate staging table in the database, which supported query folding perfectly.
Another challenge was ensuring data updates within the refresh period — for instance, late-arriving transactions. I addressed this by configuring a “detect data changes” field (like a LastModifiedDate column), which allows Power BI to automatically reprocess only partitions where data was modified recently.
The limitation with incremental refresh is that it’s only available in Power BI Premium or Pro workspaces using Import mode; it doesn’t apply to DirectQuery datasets. Also, you can’t preview incremental refresh results in Power BI Desktop — it runs only after publishing.
As an alternative, when incremental refresh isn’t available, I’ve implemented manual partitioning in Azure Analysis Services or via Dataflows — splitting data by month or year and refreshing only recent partitions programmatically through the Power BI REST API.
In summary, incremental refresh in Power BI is a powerful technique to handle large datasets efficiently. It reduces refresh time, minimizes load on the data source, and ensures users always see up-to-date data without the overhead of reloading historical information every time.