While it is true that both Power BI dataflows and Azure Data factory (ADF) performs similar ETL functions and are both powered by Power Query on cloud, there are differences in these two types of dataflow. Although you can also combine both in your solution.
Power BI dataflow ure Data Factory dataflow
Source: microsoft.com
Here are some 4 key differences:
Data sources: Power BI dataflows support many sources while ADF dataflows supports only a few.
Power Query Transformations: Power BI dataflows support all Power Query functions while ADF data flow support a limited set of functions.
Destination: Power BI dataflows destination is only limited to Dataverse or Azure Data Lake storage while ADF supports many destinations.
Scalability: Power BI dataflows is less scalable as it is dependent on the premium capacity and the use of the enhance compute engine while ADF dataflow is highly scalable.
So, which of the two types of dataflows should you use?
Your choice between which type of dataflows to use is much dependent on your user persona. If you're a citizen application developer or data analyst with small to medium scale ETL you'll find Power BI data flows more convenient. On the other hand, if you're a developer who's dealing with big data and huge dataset with large number of rows to be ingested every time, then you're better of using Azure Data factory dataflows.
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