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. Let us discuss incremental refresh (or incremental data loading) in a simple language to better understand how it works.
From a data movement standpoint, there are always two options when we transfer data from location A to location B:
- Truncation and load: We transfer the data as a whole from location A to location B. If location B has some data already, we entirely truncate the location B and reload the whole data from location A to B
- Incremental load: We transfer the data as a whole from location A to location B just once for the first time. The next time, we only load the data changes from A to B. In this approach, we never truncate B. Instead, we only transfer the data that exists in A but not in B
When we refresh the data in Power BI, we use the first approach, truncation and load, if we have not configured an incremental refresh. In Power BI, the first approach only applies to tables with Import or Dual storage modes. Previously, the Incremental load was available only in the tables with either Import or Dual storage modes. But the new announcement from Microsoft about Hybrid Tables greatly affects how Incremental load works. 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.
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. However, the Hybrid Tables are currently available on Power BI Premium Capacity and Premium Per User (PPU), not Pro. Let’s hope that Microsft will change its licensing plan for the Hybrid Tables in the future and make it available in Pro.
I will write about Hybrid Tables in a future blog post.
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. The historical range includes all data processed in the past, and the incremental range is the current range of data to process. Incremental refresh in Power BI always looks for data changes in the incremental range, not the historical range. Therefore, the incremental refresh will not notice any changes in the historical data. When we talk about the data changes, we are referring to new rows inserted, updated or deleted, however, the incremental refresh detects updated rows as deleting the rows and inserting new rows of data.
Benefits of Incremental Refresh
Configuring incremental refresh is beneficial for large tables with hundreds of millions of rows. The following are some benefits of configuring incremental refresh in Power BI:
- The data refreshes much faster than when we truncate and load the data as the incremental refresh only refreshes the incremental range
- The data refresh process is less resource-intensive than refreshing the entire data all the time
- The data refresh is less expensive and more maintainable than the non-incremental refreshes over large tables
- The incremental refresh is inevitable when dealing with massive datasets with billions of rows that do not fit into our data model in Power BI Desktop. Remember, Power BI uses in-memory data processing engine; therefore, it is improbable that our local machine can handle importing billions of rows of data into the memory
Now that we understand the basic concepts of the incremental refresh, let us see how it works in Power BI.
Implementing Incremental Refresh Policies with Power BI Desktop
We currently can configure incremental refresh in the Power BI Desktop and in Dataflows contained in a Premium Workspace. This blog post looks at the incremental refresh implementation within the Power BI Desktop.
After successfully implementing the incremental refresh policies with the desktop, we publish the model to Power BI Service. The first data refresh takes longer as we transfer all data from the data source(s) to Power BI Service for the first time. After the first load, all future data refreshes will be incremental.
How to Implement Incremental Refresh
Implementing incremental refresh in Power BI is simple. There are two generic parts of the implementation:
- Preparing some prerequisites in Power Query and defining incremental policies in the data model
- Publishing the model to Power BI Service and refreshing the dataset
Let’s briefly get to some more details to quickly understand how the implementation works.
- Preparing Prerequisites in Power Query
- We require to define two parameters with DateTime data type in Power Query Editor. The names for the two parameters are RangeStart and RangeEnd, which are reserved for defining incremental refresh policies. As you know, Power Query is case-sensitive, so the names of the parameters must be RangeStart and RangeEnd.
- The next step is to filter the table by a DateTime column using the RangeStart and RangeEnd parameters when the value of the DateTime column is between RangeStart and RangeEnd.
Continue reading “Incremental Refresh in Power BI, Part 1: Implementation in Power BI Desktop” →
- The data type of the parameters must be DateTime
- The datat tpe of the column we use for incremental refresh must be Int64 (integer) Date or DateTime.Therefore, for scenarios that our table has a smart date key instead of Date or DateTime, we have to convert the RangeStart and RangeEnd parameters to Int64
- When we filter a table using the RangeStart and RangeEnd parameters, Power BI uses the filter on the DateTime column for creating partitions on the table. So it is important to pay attention to the DateTime ranges when filtering the values so that only one filter condition must have an “equal to” on RangeStart or RangeEnd, not both