1. Fine-Grained Databricks SQL Analytics Permissions


    In Databricks, managing SQL analytics permissions allows you to control access to various resources within your workspace, such as databases, tables, views, and clusters. Fine-grained access control is essential to maintain security and governance over the data and analytics processes.

    To establish fine-grained permissions in Databricks using Pulumi, you can use the databricks.SqlPermissions resource, which allows you to assign specific privileges to different principals (users or groups).

    In this program, we will create SQL permissions for a hypothetical Databricks SQL Analytics environment. We will set up permissions for a particular clusterId, which is required. You'll typically obtain the clusterId from the cluster you have already provisioned or by creating a new one with Pulumi. In this example, we'll assume you have a clusterId on hand, and we will assign SELECT permission on a database and a table to a specific user.

    Here's how to do it:

    1. Setting up the environment: Ensure that you have the necessary Databricks workspace and user already set up. We're assuming that these are pre-existing resources in this scenario.
    2. Assigning permissions: Make use of the databricks.SqlPermissions resource to assign SELECT privileges on a database and table to a user.
    3. Using Pulumi Outputs: Permissions are set as arguments to the resource, and you can extract information such as the outcome using Pulumi export to output them once the deployment is complete.

    Let's proceed with the Pulumi program written in Python:

    import pulumi import pulumi_databricks as databricks # Configure the Pulumi Databricks provider with your workspace URL and access token # Pre-existing cluster ID on which you want to set up the permissions cluster_id = "your-cluster-id" # Define SQL Analytics permissions for a specific database and table for a user sql_permissions = databricks.SqlPermissions("sqlPermissionsExample", cluster_id=cluster_id, database="my_database", table="my_table", privilege_assignments=[{ "principal": "user@example.com", "privileges": ["SELECT"], }], ) # Export the result for reference pulumi.export("sql_permissions_id", sql_permissions.id)

    In the code snippet above, replace your-cluster-id with the actual ID of the cluster on which you wish to assign permissions. The database and table fields specify the names of the database and table to which the SELECT privilege is being assigned. The privilege_assignments list contains dictionaries, each with principal set to the user's email and privileges set to an array of privileges you want to assign.

    After running this Pulumi program, the user specified will have SELECT permissions for the specified database and table on the cluster in your Databricks environment.

    Please note that using fine-grained permissions helps you comply with the principle of least privilege, giving users only the access they need to perform their tasks.

    For more information on fine-grained SQL Analytics permissions in Databricks through Pulumi, please refer to the Databricks provider documentation.

    Make sure to have the Databricks provider configured with the necessary credentials/workspace information before attempting to execute the program, as this code assumes that the setup is already complete. If you have not set up the Databricks provider, you can find more information on the setup process in the Pulumi Databricks provider setup documentation.