1. Granular Access Control for Databricks Notebooks


    To implement granular access control for Databricks notebooks, you need to define and assign permissions to different users or groups within your organization. This allows you to control who has access to specific notebooks, and what actions they can perform on them, such as reading, executing, or modifying content.

    In Pulumi, you can manage Databricks permissions by using the databricks.Permissions resource, which is part of the pulumi_databricks provider. This resource allows you to specify access controls at a fine-grained level for various Databricks resources including notebooks.

    Here's how you define granular access control for Databricks notebooks:

    1. Define the Notebook: You need a Databricks notebook resource. If you already have an existing notebook, you can reference it by its path or ID.

    2. Specify Permissions: Define the permissions for the notebook using the databricks.Permissions resource. You can set permissions for individual users, groups, or service principals.

    3. Assign Access Controls: Specify the access levels for each entity (user/group) such as CAN_READ, CAN_RUN, CAN_EDIT, or custom permission levels as defined in your Databricks workspace.

    Let's proceed by writing a program in Python that defines access control rules for a Databricks notebook.

    import pulumi import pulumi_databricks as databricks # Create a new Databricks notebook notebook = databricks.Notebook("my-notebook", path="/Users/my.username@databricks.com/My Notebooks/my_notebook", content_base64="VGhpcyBpcyBteSBzYW1wbGUgbm90ZWJvb2sgY29udGVudA==", # Example base64-encoded content language="PYTHON" ) # Define granular access control for the Databricks notebook permissions = databricks.Permissions("my-notebook-permissions", notebook_path=notebook.path, access_controls=[ databricks.PermissionsAccessControlArgs( user_name="user1@example.com", permission_level="CAN_RUN" # User1 can only run the notebook ), databricks.PermissionsAccessControlArgs( group_name="data-scientists", permission_level="CAN_EDIT" # Members of the data-scientists group can edit the notebook ) ] ) # Export the notebook path and URL for easy access pulumi.export("notebook_path", notebook.path) pulumi.export("notebook_url", pulumi.Output.concat("https://", databricks.workspace_url, notebook.path))

    This program accomplishes the following:

    • It creates a new Databricks notebook with the provided content in base64 encoding and specifies the programming language of the notebook, which is Python in this case.

    • It then defines permissions for this notebook, granting a user named user1@example.com the ability to run the notebook and all members of the data-scientists group the ability to edit the notebook.

    • Finally, it exports the notebook's path and URL for easy reference.

    Make sure you have configured your Pulumi with the required Databricks provider and authentication.

    By following this approach, you can set up granular access control for your Databricks notebooks and manage permissions effectively as part of your infrastructure as code using Pulumi. This ensures consistent and repeatable deployment of access policies which is essential for maintaining a secure and well-governed data environment.