Prepare data with data wrangling
Data wrangling in data factory allows you to build interactive Power Query mash-ups natively in ADF and then execute those at scale inside of an ADF pipeline.
Power Query acitivty in ADF is currently avilable in public preview
Create a Power Query activity
There are two ways to create a Power Query in Azure Data Factory. One way is to click the plus icon and select Data Flow in the factory resources pane.
Previously, the data wrangling feature was located in the data flow workflow. Now, you will build your data wrangling mash-up from
New > Power query
The other method is in the activities pane of the pipeline canvas. Open the Power Query accordion and drag the Power Query activity onto the canvas.
Author a Power Query data wrangling activity
Add a Source dataset for your Power Query mash-up. You can either choose an existing dataset or create a new one. You can also select a sink dataset. You can choose one or more source datasets, but only one sink is allowed at this time. Choosing a sink dataset is optional, but at least one source dataset is required.
Click Create to open the Power Query Online mashup editor.
Author your wrangling Power Query using code-free data preparation. For the list of available functions, see transformation functions. ADF translates the M script into a data flow script so that you can execute your Power Query at scale using the Azure Data Factory data flow Spark environment.
Running and monitoring a Power Query data wrangling activity
To execute a pipeline debug run of a Power Query activity, click Debug in the pipeline canvas. Once you publish your pipeline, Trigger now executes an on-demand run of the last published pipeline. Power Query pipelines can be schedule with all existing Azure Data Factory triggers.
Go to the Monitor tab to visualize the output of a triggered Power Query activity run.
Learn how to create a mapping data flow.