1. Role-Based Access for BigQuery Data Analysts


    Creating a role-based access control (RBAC) system for data analysts working with BigQuery involves assigning the appropriate roles and permissions to users or groups, based on their job responsibilities. Google Cloud Platform (GCP) offers predefined roles for BigQuery, such as roles/bigquery.dataViewer, roles/bigquery.dataEditor, and roles/bigquery.dataOwner, which you can assign to users to allow different levels of access to your datasets and tables.

    To demonstrate the process of using Pulumi to manage role-based access for BigQuery data analysts, we'll write a program that accomplishes the following:

    1. Assigns a predefined BigQuery role to a particular user.
    2. Assigns a custom role with specific permissions suitable for a data analyst.

    Google Cloud has two modes of resources for Pulumi: gcp and google-native. The gcp provider is typically easier to use and more high-level, so we will prefer that one. However, for IAM-related tasks, we often use google-native to have a more granular control over permissions.

    In this example, we'll use the google-native provider to assign the roles/bigquery.dataViewer role to a user.

    Note: Before you begin, ensure you have the GCP provider configured for Pulumi. You should have already set up your GCP credentials for the Pulumi CLI to use.

    Here's how we'd do it with Pulumi in Python:

    import pulumi import pulumi_google_native as google_native # Project ID and the user email will be provided here. project_id = "your-gcp-project-id" user_email = "data.analyst@example.com" # Assign 'BigQuery Data Viewer' predefined role to a user. bigquery_data_viewer_role = google_native.cloudresourcemanager.v1.IAMPolicyMember("bigquery-data-viewer-role", member="user:{}".format(user_email), role="roles/bigquery.dataViewer", resource="projects/{}".format(project_id)) # Define a custom role for a BigQuery Data Analyst with specific permissions. bigquery_data_analyst_permissions = [ "bigquery.datasets.get", "bigquery.tables.list", "bigquery.tables.get", "bigquery.jobs.create" # Allows running queries ] # Custom role for BigQuery Data Analyst. custom_role = google_native.iam.v1.ProjectRole("bigquery-data-analyst-custom-role", parent=google_native.iam.v1.ProjectRoleParentArgs(pid=project_id), title="BigQuery Data Analyst Custom Role", included_permissions=bigquery_data_analyst_permissions, stage="GA") # Assign custom role to a user. custom_role_binding = google_native.cloudresourcemanager.v1.IAMPolicyMember("custom-role-binding", member="user:{}".format(user_email), role=pulumi.Output.concat("projects/", project_id, "/roles/", custom_role.id), resource="projects/{}".format(project_id)) pulumi.export("custom_role_id", custom_role.id)

    This program does the following:

    • Defines bigquery_data_viewer_role to assign the roles/bigquery.dataViewer role to the specified user. This role allows the user to view datasets, queries, and tables in BigQuery but not to modify any data.
    • Sets up a list of permissions that are relevant to a data analyst's responsibilities in bigquery_data_analyst_permissions.
    • Creates a custom role, bigquery-data-analyst-custom-role, using the permissions listed, which might be useful if you want to provide a more granular level of access compared to the predefined roles.
    • The custom_role_binding assigns the newly created custom role to the user.
    • pulumi.export is used to output the custom role's ID for future reference or for use in other parts of your infrastructure.

    Remember to replace your-gcp-project-id with your actual GCP project ID and data.analyst@example.com with the email of the user to whom you want to assign the roles. After you execute this program with Pulumi, the defined roles and permissions will be applied to your GCP resources.