Quickstart: Get started with Azure Data Factory

APPLIES TO: Azure Data Factory Azure Synapse Analytics


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Welcome to Azure Data Factory! This getting started article will let you create your first data factory and pipeline within 5 minutes. The ARM template below will create and configure everything you need to try it out. Then you only need to navigate to your demo data factory and make one more click to trigger the pipeline, which moves some sample data from one Azure blob storage to another.


If you don't have an Azure subscription, create a free account before you begin.

Video summary

The following video provides a walkthrough of the sample:

Try your first demo with one click

In your first demo scenario you will use the Copy activity in a data factory to copy an Azure blob named moviesDB2.csv from an input folder on an Azure Blob Storage to an output folder. In a real world scenario this copy operation could be between any of the many supported data sources and sinks available in the service. It could also involve transformations in the data.

Try it now with one click! After clicking the button below, the following objects will be created in Azure:

  • A data factory account
  • A pipeline within the data factory with one copy activity
  • An Azure blob storage with moviesDB2.csv uploaded into an input folder as source
  • A linked service to connect the data factory to the Azure blob storage

Step 1: Click the button to start

Select the button below to try it out! (If you clicked the one above already, you don't need to do it again.)

Try your first data factory demo

You will be redirected to the configuration page shown in the image below to deploy the template. Here, you only need to create a new resource group. (You can leave all the other values with their defaults.) Then click Review + create and click Create to deploy the resources.


The user deploying the template needs to assign a role to a managed identity. This requires permissions that can be granted through the Owner, User Access Administrator or Managed Identity Operator roles.

All of the resources referenced above will be created in the new resource group, so you can easily clean them up after trying the demo.

A screenshot of the deployment template creation dialog.

Step 2: Review deployed resources

  1. Select Go to resource group after your deployment is complete. A screenshot of the deployment complete page in the Azure portal after successfully deploying the template.

  2. In the resource group, you will see the new data factory, Azure blob storage account, and managed identity that were created by the deployment. A screenshot of the contents of the resource group created for the demo.

  3. Select the data factory in the resource group to view it. Then select the Launch Studio button to continue. A screenshot of the Azure portal on the newly created data factory page, highlighting the location of the Open Azure Data Factory Studio button.

  4. Select on the Author tab Author tab and then the Pipeline created by the template. Then check the source data by selecting Open.

    Screenshot of the Azure Data Factory Studio showing the pipeline created by the template.

  5. In the source dataset that you will see, select Browse, and note the moviesDB2.csv file, which has been uploaded into the input folder already.

    Screenshot of the source dataset highlighting the Browse button where the user can see the input file created for the demo.

    Screenshot of the contents of the input folder showing the moviesDB2.csv file used in the demo.

Step 3: Trigger the demo pipeline to run

  1. Select Add Trigger, and then Trigger Now. Screenshot of the Trigger Now button for the pipeline in the demo.
  2. In the right pane under Pipeline run, select OK.

Monitor the pipeline

  1. Select the Monitor tab Monitor tab.

  2. You can see an overview of your pipeline runs in the Monitor tab, such as run start time, status, etc.

    Screenshot of the data factory monitoring tab.

  3. In this quickstart, the pipeline has only one activity type: Copy. Click on the pipeline name and you can see the details of the copy activity's run results.

    Screenshot of the run results of a copy activity in the data factory monitoring tab.

  4. Click on details, and the detailed copy process is displayed. From the results, data read and written size are the same, and 1 file was read and written, which also proves all the data has been successfully copied to the destination.

    Screenshot of the detailed copy activity run results.

Clean up resources

You can clean up all the resources you created in this quickstart in either of two ways. You can delete the entire Azure resource group, which includes all the resources created in it. Or if you want to keep some resources intact, browse to the resource group and delete only the specific resources you want, keeping the others. For example, if you are using this template to create a data factory for use in another tutorial, you can delete the other resources but keep only the data factory.

In this quickstart, you created an Azure Data Factory containing a pipeline with a copy activity. To learn more about Azure Data Factory, continue on to the article and Learn module below.