1. Enforced Role-based Access for BigQuery ML Workflows

    Python

    Enforcing role-based access for BigQuery ML workflows in Google Cloud Platform is essential to ensure that only authorized users and services can manipulate machine learning models or access ML data. In Pulumi, this can be achieved by configuring Identity and Access Management (IAM) policies on BigQuery resources.

    In this Pulumi program, we'll configure role-based access for BigQuery by granting specific roles to different members, like users or groups. We will also define access policies at the dataset level.

    Explaining the Resources:

    1. gcp.bigquery.Dataset: This resource is used to create a new BigQuery dataset where all ML workflows and data will reside.
    2. gcp.bigquery.DatasetIamBinding: This resource is used to associate a specific role with a list of members within the context of the BigQuery dataset, granting them permissions defined by that role.
    3. gcp.bigquery.DatasetIamMember: This resource is similar to DatasetIamBinding but for adding a single member to a single role.
    4. gcp.bigquery.DatasetIamPolicy: This resource provides a way to set the entire IAM policy for a BigQuery dataset.

    The key difference between IamBinding and IamPolicy is that IamBinding allows you to add or remove individual roles, while IamPolicy sets the complete policy all at once, overwriting any existing policies.

    For our use case, we'll use gcp.bigquery.DatasetIamBinding for a clearer demonstration, which will allow us to add specific roles for users and groups to the BigQuery dataset. This ensures a more granular control over who has what kind of access.

    The Pulumi Program:

    import pulumi import pulumi_gcp as gcp # Create a BigQuery dataset for ML workflows ml_dataset = gcp.bigquery.Dataset("ml_dataset", dataset_id="ml_workflows", location="US", ) # Grant the "roles/bigquery.dataEditor" role to a specific user for the ML dataset data_editor_binding = gcp.bigquery.DatasetIamBinding("data_editor_binding", dataset_id=ml_dataset.dataset_id, role="roles/bigquery.dataEditor", members=["user:ml.data.editor@example.com"], ) # Grant the "roles/bigquery.dataViewer" role to all users in a specific group data_viewer_binding = gcp.bigquery.DatasetIamBinding("data_viewer_binding", dataset_id=ml_dataset.dataset_id, role="roles/bigquery.dataViewer", members=["group:ml.data.viewers@example.com"], ) # Grant the "roles/bigquery.user" role to another specific user user_role_binding = gcp.bigquery.DatasetIamBinding("user_role_binding", dataset_id=ml_dataset.dataset_id, role="roles/bigquery.user", members=["user:ml.user@example.com"], ) # Export the dataset ID and IAM members to see the results pulumi.export("ml_dataset_id", ml_dataset.dataset_id) pulumi.export("data_editor_member", data_editor_binding.members) pulumi.export("data_viewer_members", data_viewer_binding.members) pulumi.export("user_role_member", user_role_binding.members)

    This program does the following:

    • Initializes a new BigQuery dataset where ML workflows are supposed to run.
    • Assigns the bigquery.dataEditor role to a user, which allows them to create, update, and delete data in the dataset.
    • Assigns the bigquery.dataViewer role to a group, which allows them to view data inside the dataset.
    • Assigns the bigquery.user role to another user, which grants permissions such as running jobs, including queries, within the dataset.

    By managing IAM roles with Pulumi, you can easily control who accesses your ML workflows and related data in BigQuery, thus maintaining secure and orderly environments for your data science teams.

    Make sure to replace the member identifiers like user:ml.data.editor@example.com with the actual user or group email IDs that you want to grant access to.

    For full reference to the resources used, here are the documentation links:


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