1. Using Snowflake Tables for AI Data Versioning


    In the context of using Pulumi with Snowflake, you might want to create tables within Snowflake to manage and version data for AI applications. For example, you could be storing datasets for machine learning and need to maintain different versions of these datasets.

    Here's a Pulumi program in Python that demonstrates how you could provision a Snowflake table specifically for this purpose. The program uses the pulumi_snowflake package to interact with Snowflake resources.

    In this example, we are going to:

    1. Create a new Snowflake database called ai_db.
    2. Then, create a schema named ml_datasets within that database.
    3. After that, we'll create a table dataset_versions to keep track of different versions of datasets used for AI applications. This table will have columns suitable for data versioning, such as a unique identifier, the dataset name, the version number, the creation date, and a description.

    Before running the program, you'll need to ensure your Snowflake credentials are set up correctly, either in the Pulumi stack configuration or through environment variables that the Snowflake provider can use.

    Let's go through the Pulumi program:

    import pulumi import pulumi_snowflake as snowflake # Create a new Snowflake database for AI data ai_database = snowflake.Database("ai_db", # The name for the Snowflake database name="AI_DB", # Optional: A comment for the database comment="A Snowflake database to store AI datasets for versioning") # Create a schema for machine learning datasets within the AI database ml_datasets_schema = snowflake.Schema("ml_datasets", # The name for the schema name="ML_DATASETS", # The database you're creating the schema on database=ai_database.name, # Optional: A comment for the schema comment="A schema to organize different machine learning datasets") # Define the columns for the table dataset_columns = [ # Unique identifier for each entry snowflake.TableColumnArgs(name="id", type="NUMBER(38, 0)", nullable=False), # Name of the dataset snowflake.TableColumnArgs(name="dataset_name", type="VARCHAR", nullable=False), # Version of the dataset snowflake.TableColumnArgs(name="version", type="VARCHAR", nullable=False), # Date the dataset version was created snowflake.TableColumnArgs(name="created_at", type="TIMESTAMP_LTZ(9)"), # Description or notes about the dataset version snowflake.TableColumnArgs(name="description", type="VARCHAR"), ] # Create a table to track dataset versions for AI applications dataset_versioning_table = snowflake.Table("dataset_versions", # The name for the table name="DATASET_VERSIONS", # The schema and database the table is on schema=ml_datasets_schema.name, database=ai_database.name, # Defining the columns of the table columns=dataset_columns, # Set a primary key column for the table primaryKey=snowflake.TablePrimaryKeyArgs(keys=["id"]), # Optional: A comment for the table comment="A table to track different versions of datasets used in AI/ML applications", # Optional: Specify the data retention days for the table dataRetentionDays=90) # Output the database name, schema name, and table name # These are useful as they can be utilized by other systems integrating with the Snowflake resources pulumi.export("database_name", ai_database.name) pulumi.export("schema_name", ml_datasets_schema.name) pulumi.export("table_name", dataset_versioning_table.name)

    In the above program:

    • We begin by importing the necessary Pulumi packages.
    • The ai_database resource represents a new Snowflake database dedicated to AI data storage.
    • The ml_datasets_schema resource represents a schema within the AI database to structure our datasets.
    • We define the structure of our dataset_versioning_table through an array of snowflake.TableColumnArgs, specifying each column's name, data type, and nullable attribute.
    • Next, we instantiate the dataset_versioning_table resource, linking it to our schema and database and applying the column structure we've defined.
    • Lastly, we export the names of these resources so they can be referenced as outputs when the Pulumi application is deployed.

    Remember, when you want to apply this Pulumi program, you must have the Pulumi CLI installed, an active account on Snowflake, and the appropriate access permissions to create resources.

    For further information on the Pulumi Snowflake provider and the resources used in this program, you can refer to the official Pulumi documentation: