1. Metadata Store for ML Pipelines with PostgreSQL.


    To set up a metadata store for ML pipelines using PostgreSQL on Pulumi, you'll create a PostgreSQL database instance where you can store and manage your metadata. Pulumi enables you to declare infrastructure as code using real programming languages, and in this case, we'll use Python to describe our cloud resources.

    We'll set up the following resources:

    • A PostgreSQL server: The main database server which can host multiple databases. It's the primary component of our PostgreSQL deployment.
    • A PostgreSQL database: A specific database within the PostgreSQL server dedicated to storing the metadata for your ML pipelines.
    • Optionally, if you're concerned about access management and isolation within your PostgreSQL server, you can also set up a PostgreSQL schema that acts as a namespace within the database and contains all your objects, like tables and functions. However, this is optional and can be omitted if you're okay with the default public schema.

    Let's write a Pulumi program in Python to create a simple metadata store for ML pipelines:

    import pulumi import pulumi_postgresql as postgresql # This is Pulumi's PostgreSQL provider # PostgreSQL server must be created separately via your cloud provider or using a managed service. # Pulumi doesn't provide resources to create a low-level PostgreSQL server directly, # but it does provide resources to manage databases, schemas, roles, etc., within an existing PostgreSQL server. # Create a PostgreSQL database for storing metadata metadata_db = postgresql.Database("metadata-db", # 'name' is a required parameter: Set the name of your database name="metadata_store", # 'owner' is another required parameter: Set this to the database user that will own this database owner="service_user" ) # (Optional) Create a PostgreSQL schema within our database for better isolation # This is a logical grouping of tables under one namespace, often used for organizing data within a single database metadata_schema = postgresql.Schema("metadata-schema", # Specify the name of the schema name="ml_pipelines", # Specify the database in which to create this schema database=metadata_db.name ) # Pulumi program output # This will output the database name and schema name once the resources are created pulumi.export("database_name", metadata_db.name) pulumi.export("schema_name", metadata_schema.name)

    In the example above, we import pulumi and pulumi_postgresql. We then define two main resources - a Database called "metadata-db" and an optional Schema called "metadata-schema" within that database.

    Please note that this program assumes that you have already set up a PostgreSQL server. The Pulumi PostgreSQL plugin manages resources within an existing PostgreSQL server, not the actual server itself. You can set up a PostgreSQL server manually, via a cloud provider like AWS RDS or Google Cloud SQL, or use a managed database provider service. You'll need to manage network security and access to allow Pulumi to communicate with your database server.

    Once you have created these resources using the Pulumi CLI, you can connect to the PostgreSQL server using the metadata store database and begin creating table structures to store metadata for your ML pipelines. The data structures and designs would be informed by the needs of your applications and the structure of your ML pipeline metadata.

    Remember that production database setups might require additional considerations such as backups, high availability, disaster recovery, security, etc., which are beyond the scope of this example.