Transformation with Azure Databricks

APPLIES TO: Azure Data Factory Azure Synapse Analytics

In this tutorial, you create an end-to-end pipeline that contains the Validation, Copy data, and Notebook activities in Azure Data Factory.

• Validation ensures that your source dataset is ready for downstream consumption before you trigger the copy and analytics job.

• Copy data duplicates the source dataset to the sink storage, which is mounted as DBFS in the Azure Databricks notebook. In this way, the dataset can be directly consumed by Spark.

• Notebook triggers the Databricks notebook that transforms the dataset. It also adds the dataset to a processed folder or Azure Synapse Analytics.

For simplicity, the template in this tutorial doesn't create a scheduled trigger. You can add one if necessary.

Prerequisites

• An Azure Blob storage account with a container called sinkdata for use as a sink.

  Make note of the storage account name, container name, and access key. You'll need these values later in the template.

• An Azure Databricks workspace.

Import a notebook for Transformation

To import a Transformation notebook to your Databricks workspace:

1. Sign in to your Azure Databricks workspace, and then select Import. Your workspace path can be different from the one shown, but remember it for later.

2. Select Import from: URL. In the text box, enter https://adflabstaging1.blob.core.windows.net/share/Transformations.html.

   

3. Now let's update the Transformation notebook with your storage connection information.

   In the imported notebook, go to command 5 as shown in the following code snippet.

   • Replace <storage name>and <access key> with your own storage connection information.
   • Use the storage account with the sinkdata container.

   # Supply storageName and accessKey values  
   storageName = "<storage name>"  
   accessKey = "<access key>"  
   
   try:  
     dbutils.fs.mount(  
       source = "wasbs://sinkdata\@"+storageName+".blob.core.windows.net/",  
       mount_point = "/mnt/Data Factorydata",  
       extra_configs = {"fs.azure.account.key."+storageName+".blob.core.windows.net": accessKey})  
   
   except Exception as e:  
     # The error message has a long stack track. This code tries to print just the relevant line indicating what failed.
   
   import re
   result = re.findall(r"\^\s\*Caused by:\s*\S+:\s\*(.*)\$", e.message, flags=re.MULTILINE)
   if result:
     print result[-1] \# Print only the relevant error message
   else:  
     print e \# Otherwise print the whole stack trace.  
   

4. Generate a Databricks access token for Data Factory to access Databricks.

   1. In your Databricks workspace, select your user profile icon in the upper right.
   2. Select User Settings.
   3. Select Generate New Token under the Access Tokens tab.
   4. Select Generate.

   

   Save the access token for later use in creating a Databricks linked service. The access token looks something like dapi32db32cbb4w6eee18b7d87e45exxxxxx.

How to use this template

1. Go to the Transformation with Azure Databricks template and create new linked services for following connections.

   

   • Source Blob Connection - to access the source data.

     For this exercise, you can use the public blob storage that contains the source files. Reference the following screenshot for the configuration. Use the following SAS URL to connect to source storage (read-only access):

     https://storagewithdata.blob.core.windows.net/data?sv=2018-03-28&si=read%20and%20list&sr=c&sig=PuyyS6%2FKdB2JxcZN0kPlmHSBlD8uIKyzhBWmWzznkBw%3D

     

   • Destination Blob Connection - to store the copied data.

     In the New linked service window, select your sink storage blob.

     

   • Azure Databricks - to connect to the Databricks cluster.

     Create a Databricks-linked service by using the access key that you generated previously. You can opt to select an interactive cluster if you have one. This example uses the New job cluster option.

     

2. Select Use this template. You'll see a pipeline created.

   

Pipeline introduction and configuration

In the new pipeline, most settings are configured automatically with default values. Review the configurations of your pipeline and make any necessary changes.

1. In the Validation activity Availability flag, verify that the source Dataset value is set to SourceAvailabilityDataset that you created earlier.

   

2. In the Copy data activity file-to-blob, check the Source and Sink tabs. Change settings if necessary.

   • Source tab

   • Sink tab

3. In the Notebook activity Transformation, review and update the paths and settings as needed.

   Databricks linked service should be pre-populated with the value from a previous step, as shown:

   To check the Notebook settings:

   1. Select the Settings tab. For Notebook path, verify that the default path is correct. You might need to browse and choose the correct notebook path.

      

   2. Expand the Base Parameters selector and verify that the parameters match what is shown in the following screenshot. These parameters are passed to the Databricks notebook from Data Factory.

      

4. Verify that the Pipeline Parameters match what is shown in the following screenshot:

5. Connect to your datasets.

   Note

   In below datasets, the file path has been automatically specified in the template. If any changes required, make sure that you specify the path for both container and directory in case any connection error.

   • SourceAvailabilityDataset - to check that the source data is available.

     

   • SourceFilesDataset - to access the source data.

     

   • DestinationFilesDataset - to copy the data into the sink destination location. Use the following values:

     • Linked service - sinkBlob_LS, created in a previous step.

     • File path - sinkdata/staged_sink.

       

6. Select Debug to run the pipeline. You can find the link to Databricks logs for more detailed Spark logs.

   

   You can also verify the data file by using Azure Storage Explorer.

   Note

   For correlating with Data Factory pipeline runs, this example appends the pipeline run ID from the data factory to the output folder. This helps keep track of files generated by each run.

Related content

• Introduction to Azure Data Factory