1. Centralized Metadata Management for ML Datasets with Databricks Catalog


    Centralized metadata management for ML datasets is important for organizations that need to keep track of different datasets, their schemas, and how they are being used in machine learning pipelines. This is particularly true when dealing with big data and machine learning platforms like Databricks.

    Pulumi, along with the Databricks provider, can help you manage your metadata centrally. The key resource for this task is the databricks.Catalog, which represents a catalog in Databricks. A catalog is a container for databases (also known as schemas) and allows you to define a namespace of databases within a Databricks workspace. By using a catalog, you can group related databases together, which can be useful for organizing your datasets by project, environment, team, or any other logical grouping.

    In the following Pulumi program written in Python, we'll create a databricks.Catalog resource. This will provide us with the base for centralized metadata management in Databricks. We will also include a databricks.Metastore as the central place where metadata about datasets is stored.

    Here's a program that defines a catalog and a metastore for centralized metadata management:

    import pulumi import pulumi_databricks as databricks # A Databricks metastore where metadata about datasets will be stored. # Replace the values of the name, cloud, region, etc., accordingly. metastore = databricks.Metastore("central-metastore", name="central-metastore", cloud="aws", # Assuming AWS cloud region="us-west-1", # Replace with your region owner="owner@example.com") # A Databricks catalog that will use the defined metastore. # Catalogs are logical groupings of schemas that help organize your data. db_catalog = databricks.Catalog("ml-datasets-catalog", name="ml-datasets", metastore_id=metastore.metastore_id, owner="owner@example.com", comment="Catalog for ML datasets") # Export the IDs of our metastore and catalog so that we can reference them elsewhere as needed pulumi.export("metastore_id", metastore.metastore_id) pulumi.export("catalog_id", db_catalog.id)

    In this program:

    • We start by importing the necessary Pulumi modules.
    • We then create a Metastore, which in practice would be providing metadata management capabilities for all your ML datasets. You need to specify details about the cloud provider, region, and the owner.
    • Next, we define a Catalog that references the Metastore we just created. This catalog can be thought of as a namespace to keep your metadata organized.
    • Finally, the program exports the metastore and catalog IDs, which can be useful if you need to reference these resources in other parts of your infrastructure or in other Pulumi stacks.

    The owner field for both Metastore and Catalog resources represnts who owns the resource and typically would be set to an email of the person or a group that would manage these resources.

    Please remember to replace placeholder values with your actual data like region, owner, and details about your Databricks deployment. Also, ensure you have set up the Pulumi Databricks provider and authenticated it correctly to communicate with the Databricks service.

    Refer to the databricks.Catalog and databricks.Metastore documentation for more details on the properties and methods available for these resources.