![]() ![]() When we successfully configure the incremental refresh policies in Power BI, we always have two ranges of data the historical range and the incremental range. I will write about Hybrid Tables in a future blog post. Let’s hope that Microsft will change its licensing plan for the Hybrid Tables in the future and make it available in Pro. However, the Hybrid Tables are currently available on Power BI Premium Capacity and Premium Per User (PPU), not Pro. Incremental refresh used to be available only on Premium capacities, but from Feb 2020 onwards, it is also available in Power BI Pro with some limitations. With the Hybrid Tables, the Incremental load is available on a portion of the table when a specific partition is in Direct Query mode, while the rest of the partitions are in Import storage mode. But the new announcement from Microsoft about Hybrid Tables greatly affects how Incremental load works. Previously, the Incremental load was available only in the tables with either Import or Dual storage modes. In Power BI, the first approach only applies to tables with Import or Dual storage modes. ![]() When we refresh the data in Power BI, we use the first approach, truncation and load, if we have not configured an incremental refresh. Instead, we only transfer the data that exists in A but not in B The next time, we only load the data changes from A to B. Incremental load: We transfer the data as a whole from location A to location B just once for the first time.If location B has some data already, we entirely truncate the location B and reload the whole data from location A to B Truncation and load: We transfer the data as a whole from location A to location B.Let us discuss incremental refresh (or incremental data loading) in a simple language to better understand how it works.įrom a data movement standpoint, there are always two options when we transfer data from location A to location B: Incremental refresh, or IR, refers to loading the data incrementally, which has been around in the world of ETL for data warehousing for a long time. ![]()
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