1. Managing Databricks Job Permissions for Data Pipelines


    To manage Databricks job permissions for data pipelines, you will want to use the Permissions resource from the Databricks provider in Pulumi. This will allow you to define access control lists (ACLs) for various Databricks assets such as jobs, notebooks, clusters, and more.

    With Pulumi, you describe your infrastructure in code, which can be versioned, reused, and shared. The code below will demonstrate how to use Pulumi with the Databricks provider to set up job permissions for a data pipeline.

    Here's what we will do:

    1. Import the required Pulumi and Databricks packages.
    2. Set up a Databricks job (not covered in full detail here).
    3. Define the permissions required for the job using the Permissions resource.

    Note that you'll need to have your Databricks workspace set up and the Pulumi Databricks provider configured with the necessary credentials before running this code.

    import pulumi import pulumi_databricks as databricks # Assuming you already have a Databricks job set up, we will reference its ID. # Replace `job_id` with the actual ID of the Databricks job you want to manage permissions for. job_id = "your-databricks-job-id" # Define the access control list for the job. job_permissions = databricks.Permissions(f"{job_id}-permissions", job_id=job_id, access_controls=[ # Provide access to a specific user by the user's name and the permission level. databricks.PermissionsAccessControlArgs( user_name="example-user", permission_level="CAN_MANAGE", # Permission levels include CAN_VIEW, CAN_RUN, CAN_EDIT, CAN_MANAGE ), # Provide access to a specific group by the group's name and the permission level. databricks.PermissionsAccessControlArgs( group_name="data-scientists-group", permission_level="CAN_RUN", ), # ... add other access controls as needed. ] ) # Output the job permissions ID, which can be used for reference or in other parts of your infrastructure code. pulumi.export("job_permissions_id", job_permissions.id)

    Explanation of some important components:

    • databricks.Permissions: This is a Pulumi resource that represents Databricks permissions. Using this, you can manage access control lists for your Databricks assets.
    • access_controls: It's a list that denotes the ACLs for the job. You provide user and group names along with the level of permissions they should have.

    Permission levels you can assign are:

    • CAN_VIEW: User can view but not execute the job.
    • CAN_RUN: User can execute the job but can't modify it.
    • CAN_EDIT: User can execute and modify the job configuration.
    • CAN_MANAGE: User has all the permissions including deleting the job.

    Make sure to replace the placeholders with actual values corresponding to your Databricks setup. When you run this Pulumi program, it will configure the specified permissions for the provided Databricks job. This is essential for managing access in a team environment or when you have multiple jobs that need fine-grained access control.