1. Large Scale ETL Operations on Databricks Workspaces


    To facilitate large-scale ETL (Extract, Transform, Load) operations on Databricks, we need to set up a Databricks workspace. This workspace is where the data processing will take place, and it is the central location for all of the code and data used for ETL jobs.

    To create a Databricks workspace using Pulumi, you will use the Workspace resource from the pulumi_azure-native package. This package is the Pulumi provider for Azure that enables managing Azure resources using infrastructure as code.

    Let's start by setting up the workspace. We'll create a Databricks workspace within a given resource group. You will need to provide the following information:

    • ResourceGroupName: The name of the resource group to which the workspace belongs.
    • WorkspaceName: The name of the Databricks workspace you wish to create.
    • Location: The Azure region where the workspace will be deployed.
    • Sku: The SKU (pricing tier) of the workspace you wish to create.

    Additionally, we'll apply some configurations that are relevant to ETL operations. These might include enabling no public IP for secure connections and configuring the workspace to use customer-managed keys for encryption.

    Here is the Pulumi program written in Python that sets up the Databricks workspace on Azure:

    import pulumi import pulumi_azure_native as azure_native # Configure the Databricks workspace workspace_name = "myDatabricksWorkspace" resource_group_name = "myResourceGroup" location = "West US" # Creating the Azure Databricks Workspace databricks_workspace = azure_native.databricks.Workspace("databricksWorkspace", resource_group_name=resource_group_name, workspace_name=workspace_name, location=location, sku=azure_native.databricks.SkuArgs( name="standard" # Choose "premium" for premium tier ), # Enable No Public IP for secure connections parameters=azure_native.databricks.WorkspaceCustomParametersArgs( public_ip_name=azure_native.databricks.WorkspaceCustomStringParameterArgs( value="Disabled" )), # Configuration for customer-managed key encryption encryption=azure_native.databricks.WorkspaceEncryptionArgs( entities=azure_native.databricks.EncryptionEntitiesArgs( managed_disk=azure_native.databricks.EncryptionPropertyArgs( key_source="Microsoft.Keyvault", key_vault_properties=azure_native.databricks.KeyVaultPropertiesArgs( key_name="myKey", key_version="myKeyVersion", key_vault_uri="https://mykeyvault.vault.azure.net/" ) ) )), tags={"Environment": "Production"} ) # Export the Databricks workspace URL, which can be used for ETL operations pulumi.export('databricks_workspace_url', databricks_workspace.workspace_url)

    In this program:

    • We import the necessary Pulumi modules for Azure.
    • A Workspace is defined under the specified resource group and location.
    • We select a SKU for the workspace, which determines the pricing tier. "standard" is used here, but you can also use "premium" for more capabilities.
    • The parameters are used to disable the public IP for secure ETL operations.
    • encryption configuration is made to set up customer-managed keys for additional security.
    • At the end of the program, we export the workspace URL, which can be used to connect and manage your ETL jobs in the Databricks workspace.

    Remember to replace myDatabricksWorkspace, myResourceGroup, myKey, myKeyVersion, and https://mykeyvault.vault.azure.net/ with your actual resource names, versions, and URLs.

    Please ensure you have the Azure CLI installed and configured and that you have logged in using az login. Additionally, check to have the correct permissions to create resources in the specified Azure subscription.

    When this Pulumi program is run, it will provision the necessary resources in Azure so that you can start setting up your ETL workflows and jobs within the Databricks workspace.