1. Centralized Schema Registry for AI Pipelines using Databricks Metastore


    To create a centralized schema registry for AI pipelines, the Databricks Metastore provides a scalable and managed metastore service that allows you to unify the management of your data schemas, ensuring consistency and governance across your data and AI workloads. It is particularly well-suited for environments already using Databricks as part of their AI and data processing pipelines.

    The following program demonstrates how to deploy a Databricks Metastore in your environment using Pulumi. We will define a Metastore, configure it with necessary properties, and attach a MetastoreDataAccess to it. This will facilitate the management of data schemas that are essential for AI pipelines.

    In the example, we will:

    1. Create a Metastore, which is the centralized registry for storing metadata about databases, tables, views, and other constructs.
    2. Set up a MetastoreDataAccess policy, to define access privileges to the Metastore, such as read-only or default data access.
    3. Assign the Metastore to a workspace with MetastoreAssignment.

    Before you can run this code, ensure that you have the Pulumi CLI installed, and you're logged into the Databricks Workspace and Pulumi console. You will also need to have the appropriate Databricks provider configuration in place for Pulumi.

    Here's the Pulumi program in Python:

    import pulumi import pulumi_databricks as databricks # Create a Databricks Metastore metastore = databricks.Metastore("central-metastore", # Replace these example values with your actual cloud, region, etc. cloud="aws", region="us-west-2", storage_root="s3://my-storage-root/metastore/", # Additional properties can be configured as required ) # Specify a Metastore Data Access configuration # You may need to replace placeholder values with your AWS IAM role or equivalent access configuration data_access = databricks.MetastoreDataAccess("metastore-data-access", name="default-access", metastore_id=metastore.metastore_id, # Set to True to make this the default data access policy for the metastore is_default=True, # Replace with your IAM role or remove if not applicable aws_iam_role={ "role_arn": "arn:aws:iam::123456789012:role/AI_Pipelines_Access" } ) # Assign the Metastore to a workspace # The workspace_id should correspond to your existing Databricks workspace ID metastore_assignment = databricks.MetastoreAssignment("metastore-assignment", metastore_id=metastore.metastore_id, # Replace with your actual workspace ID workspace_id=1234567890 ) # Export the metastore ID pulumi.export("metastore_id", metastore.metastore_id)

    The program begins by importing the necessary Pulumi and Pulumi Databricks modules. Then, it creates the Metastore resource, providing the cloud and region where the metastore is to be located, and the S3 storage root URL where the metastore will keep its data.

    Next, it sets up a MetastoreDataAccess policy, which defines the access control to the metastore. In this example, the policy is set up with AWS IAM role-based access, but this can be customized as per your cloud provider and access requirements.

    After that, the MetastoreAssignment resource links the metastore with a Databricks workspace by using the workspace ID. By assigning it to a workspace, you enable the AI pipelines running within that workspace to utilize the unified metastore for schema management.

    We also export the metastore_id using Pulumi's export feature, which could be useful if you need to reference this metastore's ID in other parts of your infrastructure as code.

    Remember to replace placeholders like the AWS IAM role ARN and the workspace ID with actual values particular to your setup. Also, handle the security-sensitive information, such as IAM role ARNs, using secret management practices suitable for your organization.