1. Large-Scale ETL Jobs for Preprocessing AI Workloads in Databricks


    To create large-scale ETL (Extract, Transform, Load) jobs for preprocessing AI workloads in Databricks using Pulumi, you typically need to set up a Databricks workspace, clusters, and jobs that orchestrate the ETL process. Let's walk through creating a basic setup to get you started with Pulumi in Python.

    Setting up the Databricks Workspace

    You'll need a Databricks workspace to host your clusters and jobs. This is a platform provided by Databricks on a cloud provider of your choice (AWS, Azure, GCP).

    Creating Clusters

    In Databricks, clusters are the compute resources you use to run your notebooks, libraries, and jobs. They can auto-scale according to the workload requirements.

    Defining Jobs

    Jobs in Databricks are used to execute notebooks, JARs, Python scripts, or custom Spark submitting jobs. They can be scheduled or run on-demand and may depend on the successful completion of other jobs.

    Implementing the Databricks ETL Pipeline with Pulumi

    Below is the basic structure of a Pulumi program in Python that sets up a Databricks environment including workspace, cluster, and an ETL job:

    import pulumi import pulumi_databricks as databricks # Define a Databricks Workspace workspace = databricks.Workspace("my-databricks-workspace", sku="premium", # Choose the SKU that suits your requirements. location="westus", # Select the region that is appropriate. managed_resource_group_id="/subscriptions/{subscription-id}/resourceGroups/{managed-resource-group-name}", ) # Define a Databricks cluster cluster = databricks.Cluster("my-databricks-cluster", num_workers=4, # Define the number of workers you need for the cluster. node_type_id="Standard_D3_v2", # Choose the node type as per your requirements. spark_version="7.3.x-scala2.12", # Choose appropriate Spark and Scala versions. ) # Define a Databricks job for ETL purposes job = databricks.Job("my-etl-job", existing_cluster_id=cluster.id, # Referencing the defined cluster. new_task=databricks.JobArgs( spark_jar_task=databricks.JobSparkJarTaskArgs( parameters=["job_arg1", "job_arg2"], # Arguments for your Spark job. main_class_name="com.example.etl.Main", # The main class to run for the job. # The URI to fetch the jar from (example: dbfs:/your/path/your-file.jar) jar_uri="dbfs:/path/to/your/etl.jar", ) ), ) # Export the Databricks workspace URL for easy access pulumi.export("workspace_url", workspace.workspace_url)

    This program initializes a stack with a Databricks workspace, a cluster, and an ETL job. You can adjust the parameters for the workspace, cluster, and job to match the scale and requirements of your AI workload preprocessing.

    Make sure to replace placeholder values such as {subscription-id}, {managed-resource-group-name}, job_arg1, job_arg2, and /path/to/your/etl.jar with actual values from your specific environment or setup.

    Run this Pulumi program using the Pulumi CLI by executing pulumi up which will provision the defined resources in the cloud. Be aware that running this code may incur costs from your cloud provider, as it provisions real cloud resources.

    With this basic setup, you can start submitting your ETL jobs for preprocessing AI workloads in Databricks. You can enhance the pipeline by adding additional configurations, dependencies, and job scheduling as required by your use case.