1. Large Scale ETL Pipelines in Databricks Workspace


    To set up large scale ETL (Extract, Transform, Load) pipelines in a Databricks workspace using Pulumi, you would typically start by provisioning a Databricks workspace where you can run your ETL jobs. Then, within that workspace, you can create jobs, clusters, and any other resources necessary for your ETL processes.

    Let's create a Databricks workspace on Azure using Pulumi. We'll then configure it for ETL by ensuring that we have the necessary compute resources. Keep in mind that specific ETL tasks, job definitions, and data connections will be defined within Databricks itself or through additional automation scripts that would interface with Databricks' API.

    In this example, we're using the azure-native Pulumi provider to create an Azure Databricks workspace. azure-native is preferred over the older azure provider because it is auto-generated from the Azure Resource Manager REST APIs and always up-to-date with the latest Azure features.

    Here's what the Pulumi Python program would look like:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup("resource_group") # Create an Azure Databricks Workspace databricks_workspace = azure_native.databricks.Workspace("databricksWorkspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.databricks.WorkspaceSkuArgs( name="standard", # The type of Databricks workspace to deploy (e.g., standard, premium) ), # Additional parameters can be provided based on the specific requirements for the workspace. # For example, setting up the network security rules, tags, or customer-managed keys for encryption. ) # Export the Databricks Workspace URL which you can use to navigate to your Databricks workspace in Azure. pulumi.export('databricks_workspace_url', databricks_workspace.workspace_url)

    In this program:

    • First, we create an Azure Resource Group, which is a container that holds related resources for an Azure solution.
    • Next, we provision an Azure Databricks workspace within the created resource group by using the Workspace resource from the azure-native Pulumi provider. Here, we specify the SKU to use for the workspace. SKUs determine factors like availability, pricing, hardware specs, etc.

    To automate the process further and create ETL pipelines, you could include additional resources such as Job or Cluster definitions using Databricks' REST API or Terraform providers since native Pulumi support for these might be limited or would involve additional custom implementations.

    You can then deploy this program using Pulumi's CLI by running pulumi up, which will show you the proposed infrastructure changes. Upon confirmation, Pulumi will proceed to create the resources in Azure.

    After the infrastructure is deployed, you would typically use the Databricks workspace URL to navigate to the workspace in the Azure portal, where you can further set up your ETL processes, notebooks, and cluster configurations, and manage the full lifecycle of your data processing tasks.