1. Scalable Data Engineering for AI on Databricks


    To set up a scalable data engineering environment for AI on Databricks using Pulumi, you would generally create a Databricks workspace, set up clusters, and define jobs that run on those clusters to process your data. Databricks is a platform that provides a collaborative environment with a powerful set of tools to process big data and run machine learning models at scale.

    For this setup using Pulumi, here's what we would do:

    1. Databricks Workspace: A workspace is the foundational block in Databricks where data engineering and machine learning tasks are executed. It contains notebooks, data, and configurations. We'll create a workspace using the Workspace resource.

    2. Databricks Cluster: Clusters are groups of compute resources in Databricks where you can run your data processing tasks. With Pulumi, we'll define a Cluster resource to set up a scalable cluster. This cluster can autoscale based on the workload, which is particularly useful in a demanding data engineering scenario.

    3. Databricks Jobs: Jobs are tasks or a set of tasks that execute code on your clusters. For example, this could be an ETL (extract, transform, load) task that prepares your data for machine learning. We define these using the Job resource.

    Let's create a simple Pulumi program in Python that sets up a Databricks workspace, cluster, and a job. We're assuming that you've already set up your Azure Provider with Pulumi and that you have an account with Azure Databricks.

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Databricks workspace databricks_workspace = azure_native.databricks.Workspace("aiDataEngineeringWorkspace", # Define location, SKU, and other properties as needed: location="East US", sku=azure_native.databricks.SkuArgs(name="standard"), resource_group_name="myResourceGroup", # Ensure you have a resource group created managed_resource_group_id="/subscriptions/{subscription_id}/resourceGroups/{managed_resource_group_name}" ) # For the purpose of this example, let's assume you have a Pulumi component that deploys # Databricks Clusters and Jobs which wrap around the REST API or use the databricks provider, # since Databricks Resources are not fully supported natively in Pulumi for Azure as of my last training cut-off in 2023. # Note: The actual component/API may differ. # Now, using the workspace details we can configure clusters and jobs # For instance: # Create a Databricks cluster within the workspace databricks_cluster = databricks.Cluster("aiDataCluster", cluster_name="data-processing-cluster", spark_version="7.3.x-scala2.12", node_type_id="Standard_DS3_v2", # Choose node types based on your processing needs num_workers=2, # Start with 2 workers, can set up autoscaling if necessary autoscale=databricks.ClusterAutoscaleArgs( min_workers=2, max_workers=50 # Autoscale up to 50 workers ), runtime_version="7.3.x-runtime", # Choose an appropriate runtime version ) # Define a Databricks job to run on the cluster databricks_job = databricks.Job("etlJob", job_name="daily-etl", cluster_id=databricks_cluster.cluster_id, notebook_task=databricks.JobNotebookTaskArgs( notebook_path="/Users/me/my-notebooks/etl-notebook", base_parameters={"date": "2022-01-01"} # Replace with dynamic parameters as needed ), schedule=databricks.JobScheduleArgs( quartz_cron_expression="0 0 * * * ?", # Run the job every day at midnight timezone_id="America/Los_Angeles" # Set your timezone ) ) # Export the Databricks Workspace URL so that you can access it easily. pulumi.export("databricksWorkspaceUrl", databricks_workspace.uiDefinitionUri)

    This program sets up a foundational Databricks environment. It creates a workspace and then sets up a cluster and a job within that workspace. The job is scheduled to run a notebook daily. This setup is scalable, as the cluster is configured to autoscale based on the workload.

    Remember to replace placeholders such as myResourceGroup and the subscription IDs with your specific values. Also, ensure you have the necessary permissions on Azure to create these resources.

    Please note that the Databricks-related resources (databricks.Cluster, databricks.Job, and databricks.JobNotebookTaskArgs) used in this program are illustrative. The actual resources might need to be instantiated differently depending on the Databricks provider for Pulumi or using the REST API directly if the native resources are not available in Pulumi. Check the Pulumi Registry for updated information on Azure Databricks resources.