How can I learn data wrangling

Prepare data with data wrapping

  • 2 minutes to read

APPLIES TO: Azure Data Factory Azure Synapse Analytics

Using data wrapping in Data Factory, you can create interactive Power Query mash-ups natively in ADF and then run them on a large scale in an ADF pipeline.


Power Query activity in ADF is currently available in public preview

Create a Power Query Activity

There are two ways to create a Power Query in Azure Data Factory. One method is to click the plus icon and select the option in the Factory Resources area Data flow out.


The data wrapping feature was previously in the data flow workflow. Now create your data wrapping mash-up.

The other option is in the Activities area of ​​the pipeline canvas. Open the accordion element Power Queryand drag the Power Query-Activity on the canvas.

Create a Power Query data wrapping activity

Insert Source dataset for your Power Query mash-up. Either select an existing dataset or create a new one. You can also select a sink dataset. You can select one or more source datasets, but only one sink is currently allowed. Choosing a sink dataset is optional, but at least one source dataset is required.

click on Createto open the Power Query Online mashup editor.

Build your wrangling power query using code-free data prep. For the list of available functions, see Transformation Functions. ADF translates the M-Script into a data flow script so that you can run your Power Query on a large scale using the Azure Data Factory data flow Spark environment.

Run and monitor a Power Query data wrapping activity

To start running a Power Query activity to debug a pipeline, click on the pipeline canvas Debug. Now that you've published your pipeline, you're ready to go with Trigger now execute on-demand execution of the last published pipeline. Power Query pipelines can be scheduled using any existing Azure Data Factory triggers.

Switch to the tab Monitorto visualize the output of a triggered Power Query activity.

Next Steps

Learn how to create a mapping data flow.