Transform data securely by using mapping data flow
APPLIES TO: Azure Data Factory Azure Synapse Analytics
If you're new to Azure Data Factory, see Introduction to Azure Data Factory.
In this tutorial, you'll use the Data Factory user interface (UI) to create a pipeline that copies and transforms data from an Azure Data Lake Storage Gen2 source to a Data Lake Storage Gen2 sink (both allowing access to only selected networks) by using mapping data flow in Data Factory Managed Virtual Network. You can expand on the configuration pattern in this tutorial when you transform data by using mapping data flow.
In this tutorial, you do the following steps:
- Create a data factory.
- Create a pipeline with a data flow activity.
- Build a mapping data flow with four transformations.
- Test run the pipeline.
- Monitor a data flow activity.
- Azure subscription. If you don't have an Azure subscription, create a free Azure account before you begin.
- Azure storage account. You use Data Lake Storage as source and sink data stores. If you don't have a storage account, see Create an Azure storage account for steps to create one. Ensure the storage account allows access only from selected networks.
The file that we'll transform in this tutorial is moviesDB.csv, which can be found at this GitHub content site. To retrieve the file from GitHub, copy the contents to a text editor of your choice to save it locally as a .csv file. To upload the file to your storage account, see Upload blobs with the Azure portal. The examples will reference a container named sample-data.
Create a data factory
In this step, you create a data factory and open the Data Factory UI to create a pipeline in the data factory.
Open Microsoft Edge or Google Chrome. Currently, only Microsoft Edge and Google Chrome web browsers support the Data Factory UI.
On the left menu, select Create a resource > Analytics > Data Factory.
On the New data factory page, under Name, enter ADFTutorialDataFactory.
The name of the data factory must be globally unique. If you receive an error message about the name value, enter a different name for the data factory (for example, yournameADFTutorialDataFactory). For naming rules for Data Factory artifacts, see Data Factory naming rules.
Select the Azure subscription in which you want to create the data factory.
For Resource Group, take one of the following steps:
- Select Use existing, and select an existing resource group from the drop-down list.
- Select Create new, and enter the name of a resource group.
To learn about resource groups, see Use resource groups to manage your Azure resources.
Under Version, select V2.
Under Location, select a location for the data factory. Only locations that are supported appear in the drop-down list. Data stores (for example, Azure Storage and Azure SQL Database) and computes (for example, Azure HDInsight) used by the data factory can be in other regions.
After the creation is finished, you see the notice in the Notifications center. Select Go to resource to go to the Data Factory page.
Select Open Azure Data Factory Studio to launch the Data Factory UI in a separate tab.
Create an Azure IR in Data Factory Managed Virtual Network
In this step, you create an Azure IR and enable Data Factory Managed Virtual Network.
In the Data Factory portal, go to Manage, and select New to create a new Azure IR.
On the Integration runtime setup page, choose what integration runtime to create based on required capabilities. In this tutorial, select Azure, Self-Hosted and then click Continue.
Select Azure and then click Continue to create an Azure Integration runtime.
Under Virtual network configuration (Preview), select Enable.
Create a pipeline with a data flow activity
In this step, you'll create a pipeline that contains a data flow activity.
On the home page of Azure Data Factory, select Orchestrate.
In the properties pane for the pipeline, enter TransformMovies for the pipeline name.
In the Activities pane, expand Move and Transform. Drag the Data Flow activity from the pane to the pipeline canvas.
In the Adding data flow pop-up, select Create new data flow and then select Mapping Data Flow. Select OK when you're finished.
Name your data flow TransformMovies in the properties pane.
In the top bar of the pipeline canvas, slide the Data Flow debug slider on. Debug mode allows for interactive testing of transformation logic against a live Spark cluster. Data Flow clusters take 5-7 minutes to warm up and users are recommended to turn on debug first if they plan to do Data Flow development. For more information, see Debug Mode.
Build transformation logic in the data flow canvas
After you create your data flow, you'll be automatically sent to the data flow canvas. In this step, you'll build a data flow that takes the moviesDB.csv file in Data Lake Storage and aggregates the average rating of comedies from 1910 to 2000. You'll then write this file back to Data Lake Storage.
Add the source transformation
In this step, you set up Data Lake Storage Gen2 as a source.
In the data flow canvas, add a source by selecting the Add Source box.
Name your source MoviesDB. Select New to create a new source dataset.
Select Azure Data Lake Storage Gen2, and then select Continue.
Select DelimitedText, and then select Continue.
Name your dataset MoviesDB. In the linked service drop-down, select New.
In the linked service creation screen, name your Data Lake Storage Gen2 linked service ADLSGen2 and specify your authentication method. Then enter your connection credentials. In this tutorial, we're using Account key to connect to our storage account.
Make sure you enable Interactive authoring. It might take a minute to be enabled.
Select Test connection. It should fail because the storage account doesn't enable access into it without the creation and approval of a private endpoint. In the error message, you should see a link to create a private endpoint that you can follow to create a managed private endpoint. An alternative is to go directly to the Manage tab and follow instructions in this section to create a managed private endpoint.
Keep the dialog box open, and then go to your storage account.
Follow instructions in this section to approve the private link.
Go back to the dialog box. Select Test connection again, and select Create to deploy the linked service.
On the dataset creation screen, enter where your file is located under the File path field. In this tutorial, the file moviesDB.csv is located in the container sample-data. Because the file has headers, select the First row as header check box. Select From connection/store to import the header schema directly from the file in storage. Select OK when you're finished.
If your debug cluster has started, go to the Data Preview tab of the source transformation and select Refresh to get a snapshot of the data. You can use the data preview to verify your transformation is configured correctly.
Create a managed private endpoint
If you didn't use the hyperlink when you tested the preceding connection, follow the path. Now you need to create a managed private endpoint that you'll connect to the linked service you created.
Go to the Manage tab.
The Manage tab might not be available for all Data Factory instances. If you don't see it, you can access private endpoints by selecting Author > Connections > Private Endpoint.
Go to the Managed private endpoints section.
Select + New under Managed private endpoints.
Select the Azure Data Lake Storage Gen2 tile from the list, and select Continue.
Enter the name of the storage account you created.
After a few seconds, you should see that the private link created needs an approval.
Select the private endpoint that you created. You can see a hyperlink that will lead you to approve the private endpoint at the storage account level.
Approval of a private link in a storage account
In the storage account, go to Private endpoint connections under the Settings section.
Select the check box by the private endpoint you created, and select Approve.
Add a description, and select yes.
Go back to the Managed private endpoints section of the Manage tab in Data Factory.
After about a minute, you should see the approval appear for your private endpoint.
Add the filter transformation
Next to your source node on the data flow canvas, select the plus icon to add a new transformation. The first transformation you'll add is a Filter.
Name your filter transformation FilterYears. Select the expression box next to Filter on to open the expression builder. Here you'll specify your filtering condition.
The data flow expression builder lets you interactively build expressions to use in various transformations. Expressions can include built-in functions, columns from the input schema, and user-defined parameters. For more information on how to build expressions, see Data flow expression builder.
In this tutorial, you want to filter movies in the comedy genre that came out between the years 1910 and 2000. Because the year is currently a string, you need to convert it to an integer by using the
toInteger()function. Use the greater than or equal to (>=) and less than or equal to (<=) operators to compare against the literal year values 1910 and 2000. Union these expressions together with the and (&&) operator. The expression comes out as:
toInteger(year) >= 1910 && toInteger(year) <= 2000
To find which movies are comedies, you can use the
rlike()function to find the pattern 'Comedy' in the column genres. Union the
rlikeexpression with the year comparison to get:
toInteger(year) >= 1910 && toInteger(year) <= 2000 && rlike(genres, 'Comedy')
If you have a debug cluster active, you can verify your logic by selecting Refresh to see the expression output compared to the inputs used. There's more than one right answer on how you can accomplish this logic by using the data flow expression language.
Select Save and finish after you're finished with your expression.
Fetch a Data Preview to verify the filter is working correctly.
Add the aggregate transformation
The next transformation you'll add is an Aggregate transformation under Schema modifier.
Name your aggregate transformation AggregateComedyRating. On the Group by tab, select year from the drop-down box to group the aggregations by the year the movie came out.
Go to the Aggregates tab. In the left text box, name the aggregate column AverageComedyRating. Select the right expression box to enter the aggregate expression via the expression builder.
To get the average of column Rating, use the
avg()aggregate function. Because Rating is a string and
avg()takes in a numerical input, we must convert the value to a number via the
toInteger()function. This expression looks like:
Select Save and finish after you're finished.
Go to the Data Preview tab to view the transformation output. Notice only two columns are there, year and AverageComedyRating.
Add the sink transformation
Next, you want to add a Sink transformation under Destination.
Name your sink Sink. Select New to create your sink dataset.
On the New dataset page, select Azure Data Lake Storage Gen2 and then select Continue.
On the Select format page, select DelimitedText and then select Continue.
Name your sink dataset MoviesSink. For linked service, choose the same ADLSGen2 linked service you created for source transformation. Enter an output folder to write your data to. In this tutorial, we're writing to the folder output in the container sample-data. The folder doesn't need to exist beforehand and can be dynamically created. Select the First row as header check box, and select None for Import schema. Select OK.
Now you've finished building your data flow. You're ready to run it in your pipeline.
Run and monitor the data flow
You can debug a pipeline before you publish it. In this step, you trigger a debug run of the data flow pipeline. While the data preview doesn't write data, a debug run will write data to your sink destination.
Go to the pipeline canvas. Select Debug to trigger a debug run.
Pipeline debugging of data flow activities uses the active debug cluster but still takes at least a minute to initialize. You can track the progress via the Output tab. After the run is successful, select the eyeglasses icon for run details.
On the details page, you can see the number of rows and the time spent on each transformation step.
Select a transformation to get detailed information about the columns and partitioning of the data.
If you followed this tutorial correctly, you should have written 83 rows and 2 columns into your sink folder. You can verify the data is correct by checking your blob storage.
In this tutorial, you used the Data Factory UI to create a pipeline that copies and transforms data from a Data Lake Storage Gen2 source to a Data Lake Storage Gen2 sink (both allowing access to only selected networks) by using mapping data flow in Data Factory Managed Virtual Network.
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