1. Centralized Metadata Management for ML Pipelines on Databricks


    To set up a centralized metadata management system for machine learning (ML) pipelines on Databricks using Pulumi, you'll need to create a metastore that will serve as the central repository for metadata.

    A metastore in Databricks is a database for storing metadata about tables, databases, and other artifacts used in Databricks workspaces. It supports various operations that are essential for managing big data and ML pipelines, such as schema definition, partitioning information, and data lineage.

    To achieve this with Pulumi, we'll use databricks.Metastore, which allows you to create and manage your metastore configurations programmatically.

    Below is a Pulumi program in Python that illustrates how you would create a metastore on Databricks:

    import pulumi import pulumi_databricks as databricks # Create a new Databricks Metastore. metastore = databricks.Metastore("central-metastore", name="centralized-metastore", cloud="aws", # Specify the cloud provider where Databricks is running. owner="<owner-email>", # Replace with the owner's email. region="<region>", # Specify the region where the metastore should be created. storage_root="<storage-root-path>" # Replace with the S3 path to store the metastore data. ) # Output the Metastore ID pulumi.export("metastore_id", metastore.metastore_id)

    In the above code:

    • We import the necessary Pulumi libraries, including the Databricks provider.
    • We create a Metastore resource using the databricks.Metastore class, specifying essential properties like the metastore name, cloud provider, owner, region, and storage root.
    • We export the metastore ID for further reference, which can be useful if you need to use this metastore within other resources or outputs.

    You should replace the placeholder values (<owner-email>, <region>, and <storage-root-path>) with actual values that are specific to your environment and requirements.

    After deploying this code with the Pulumi CLI, your Databricks environment will have a centralized metastore configured, which you can then use across your ML pipelines to manage metadata efficiently.

    To take this a step further, you could expand your Pulumi program to create additional resources that utilize the metastore, such as databases, tables, or ML pipelines that reference the metastore for metadata storage and retrieval.

    Remember to set up your environment with appropriate Databricks credentials before running this Pulumi program. This usually includes setting up a Databricks access token and workspace URL as environment variables or in the Pulumi stack configuration.