If Dataflows Can Do Everything a Copy Activity Can (and More), Why Use Copy Activity?

[HamidBee]

Hi everyone,

I’m working with data ingestion and transformation in Microsoft Fabric and noticed that Dataflows seem to offer a lot more flexibility in terms of connecting to various data sources and performing transformations. Given that Dataflows can connect to a broader range of sources and seem capable of more complex transformations, is there any advantage to using Copy Activity instead?

Is Copy Activity preferable in certain scenarios, such as faster data transfer performance, or are there specific use cases where it might be better suited than Dataflows? I’d love to understand why and when to choose one over the other.

Thanks for your insights.

[NandanHegde · Accepted solution]

Copy activity needs to be used in case if one needs to transfer data as is between source and sink with no transformations and is much more quicker than dataflow. 

Also copy activity supports multiple sink types whereas dataflow is restricted to only specific sinks

[HamidBee]

Thanks for sharing this information.

[OktayPamuk80]

The answer is always, it depends. With millions of rows you want to have a performant and compute unit efficient process. Dataflows does support fast copy for some sources, but it will require more compute units by comparison to copy activity or notebooks.

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[mattiasdesmet]

Fabric decision guide - copy activity, dataflow, or Spark - Microsoft Fabric | Microsoft Learn

[mattiasdesmet]

Copy Activity is recommended for ingestion of data as it is much more performant than Dataflows. If you don't need to do any manipulations, Copy Activity is recommended. Once the data is in your data platform and you need to transform it, Dataflows, Notebooks etc are the options to work with data at scale.