Review the configurations of your pipeline and make any necessary changes. In the new pipeline, most settings are configured automatically with default values. This example uses the New job cluster option. You can opt to select an interactive cluster if you have one. In the New linked service window, select your sink storage blob.Īzure Databricks - to connect to the Databricks cluster.Ĭreate a Databricks-linked service by using the access key that you generated previously. Use the following SAS URL to connect to source storage (read-only access):ĭestination Blob Connection - to store the copied data. Reference the following screenshot for the configuration. Source Blob Connection - to access the source data.įor this exercise, you can use the public blob storage that contains the source files. Go to the Transformation with Azure Databricks template and create new linked services for following connections. The access token looks something like dapi32db32cbb4w6eee18b7d87e45exxxxxx. Save the access token for later use in creating a Databricks linked service. Select Generate New Token under the Access Tokens tab.In your Databricks workspace, select your user profile icon in the upper right.Generate a Databricks access token for Data Factory to access Databricks. Print e \# Otherwise print the whole stack trace. Print result \# Print only the relevant error message This code tries to print just the relevant line indicating what failed. # The error message has a long stack track. Use the storage account with the sinkdata container.Įxtra_configs = ).Replace and with your own storage connection information.In the imported notebook, go to command 5 as shown in the following code snippet. Now let's update the Transformation notebook with your storage connection information. Your workspace path can be different from the one shown, but remember it for later. Sign in to your Azure Databricks workspace, and then select Import. To import a Transformation notebook to your Databricks workspace: You'll need these values later in the template. Make note of the storage account name, container name, and access key. You can add one if necessary.Īn Azure Blob storage account with a container called sinkdata for use as a sink. It also adds the dataset to a processed folder or Azure Synapse Analytics.įor simplicity, the template in this tutorial doesn't create a scheduled trigger. Notebook triggers the Databricks notebook that transforms the dataset. In this way, the dataset can be directly consumed by Spark. Validation ensures that your source dataset is ready for downstream consumption before you trigger the copy and analytics job.Ĭopy data duplicates the source dataset to the sink storage, which is mounted as DBFS in the Azure Databricks notebook. In this tutorial, you create an end-to-end pipeline that contains the Validation, Copy data, and Notebook activities in Azure Data Factory. Microsoft Fabric covers everything from data movement to data science, real-time analytics, business intelligence, and reporting. Try out Data Factory in Microsoft Fabric, an all-in-one analytics solution for enterprises.
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