1. Defining Access Control for Snowflake Machine Learning Workstreams


    Access control in Snowflake is an essential aspect of data governance and security, as it ensures that only authorized users and roles have the ability to access and operate on specific resources like databases, schemas, tables, and more. In Snowflake, you typically manage access control through the use of Roles and Grants. Pulumi helps you define this infrastructure as code, so you can create, update, and manage your access policies programmatically and repeatably.

    In this example, we will set up a basic access control scenario using Pulumi and the Snowflake provider. We will define a Snowflake table and grant specific usage privileges to a predefined role.

    Here's an outline of what this Pulumi program will do:

    1. Define a Snowflake Database and Schema.
    2. Create a Snowflake Table within that Schema.
    3. Define a Snowflake Role.
    4. Grant specific privileges on the Table to the Role.

    Here's a detailed walk-through of the Python program to set this up:

    import pulumi import pulumi_snowflake as snowflake # Create a new Snowflake database database = snowflake.Database("analytics-database", name="ANALYTICS_DB", # Naming the database ANALYTICS_DB ) # Create a schema within the database schema = snowflake.Schema("public-schema", database=database.name, # This places the schema in the previously created database name="PUBLIC", # Naming the schema PUBLIC which is a default schema in Snowflake ) # Define a Snowflake table within the schema table = snowflake.Table("ml-workstream-table", database=database.name, schema=schema.name, name="ML_WORKSTREAM", # Naming the table ML_WORKSTREAM columns=[ # Define columns for the table snowflake.TableColumnArgs( name="ID", type="NUMBER(38,0)", ), snowflake.TableColumnArgs( name="DATA", type="VARIANT", ), snowflake.TableColumnArgs( name="CREATED_AT", type="TIMESTAMP_LTZ(9)", ), ], ) # Create a Snowflake role role = snowflake.Role("ml-engineer-role", name="ML_ENGINEER", # Naming the role ML_ENGINEER ) # Grant usage privileges on the schema to the role schema_grant = snowflake.SchemaGrant("schema-grant", database_name=database.name, schema_name=schema.name, roles=[role.name], privilege="USAGE", # Granting USAGE privilege so the role can access the schema ) # Grant select privileges on the table to the role table_grant = snowflake.TableGrant("table-grant", database_name=database.name, schema_name=schema.name, table_name=table.name, roles=[role.name], privilege="SELECT", # Granting SELECT privilege so the role can read data from the table ) # Export the names of the created resources pulumi.export("database_name", database.name) pulumi.export("schema_name", schema.name) pulumi.export("table_name", table.name) pulumi.export("role_name", role.name)

    In the example above, we start by importing the necessary libraries. The pulumi_snowflake package contains all the resources needed to interact with Snowflake.

    Next, we create a database and a public schema, followed by a table under that schema with three defined columns. The snowflake.TableColumnArgs class allows us to define properties such as column name and data type.

    After defining the database resources, we create a new role ML_ENGINEER. This role will be granted specific privileges on the schema and table.

    With the snowflake.SchemaGrant and snowflake.TableGrant resources, we grant the USAGE privilege on the schema and the SELECT privilege on the table respectively to the ML_ENGINEER role. These grants are essential to access control as they define what operations the role can perform on the given resources.

    Finally, we use pulumi.export to output the names of the resources created. This can be helpful for reference in outputs when running pulumi up which deploys your infrastructure.

    To run this program, you'll need to have the Pulumi CLI installed, as well as the necessary Snowflake provider configuration for Pulumi to connect to your Snowflake account. You also need to have credentials with the necessary permissions to create databases, schemas, tables, and roles in Snowflake.

    Remember, with Pulumi, this code not only defines your Snowflake resources but also manages the deployment and changes over time in a repeatable and predictable manner.