1. Autonomous Access Controls for GCP BigQuery with Service Accounts


    In Google Cloud Platform (GCP), BigQuery is a fully-managed data warehouse that enables super-fast SQL queries using the processing power of Google's infrastructure. To control access autonomously, we can create service accounts which represent non-human users that services can use to interact with other Google Cloud services.

    To achieve autonomous access controls for GCP BigQuery, we need to accomplish a few tasks:

    1. Create a service account that will interact with BigQuery. This represents the autonomous entity which will have control access.
    2. Grant appropriate roles to this service account to manage BigQuery datasets and tables. This can be done by attaching IAM policies to the service account.
    3. Create a BigQuery dataset which the service account can access.
    4. Set Access Control Lists (ACLs) on the BigQuery dataset to define how different entities (users, groups, service accounts) can interact with the data.

    Here's a Pulumi Python program that illustrates how to create a service account, assign it roles, and set up a BigQuery dataset with access controls:

    import pulumi import pulumi_gcp as gcp # Create a service account for BigQuery bigquery_service_account = gcp.serviceaccount.Account("bigqueryServiceAccount", account_id="bigquery-access", display_name="BigQuery Service Account") # Assign roles to the service account to control BigQuery resources bigquery_data_viewer_role = gcp.projects.IAMBinding("bigqueryDataViewerRole", role="roles/bigquery.dataViewer", members=[pulumi.Output.concat("serviceAccount:", bigquery_service_account.email)]) bigquery_data_editor_role = gcp.projects.IAMBinding("bigqueryDataEditorRole", role="roles/bigquery.dataEditor", members=[pulumi.Output.concat("serviceAccount:", bigquery_service_account.email)]) bigquery_user_role = gcp.projects.IAMBinding("bigqueryUserRole", role="roles/bigquery.user", members=[pulumi.Output.concat("serviceAccount:", bigquery_service_account.email)]) # Create a BigQuery Dataset bigquery_dataset = gcp.bigquery.Dataset("bigqueryDataset", dataset_id="my_dataset", access=[ { # Grant the service account the role of owner on this dataset "role": "OWNER", "userByEmail": bigquery_service_account.email, }, { # Optional: You could also share this dataset with other users/groups here "role": "READER", "domain": "example.com", } ]) # Export the service account email and the dataset ID pulumi.export("service_account_email", bigquery_service_account.email) pulumi.export("dataset_id", bigquery_dataset.dataset_id)

    This program starts by importing the required Pulumi modules for GCP. It then proceeds to create a new service account, bigqueryServiceAccount, which will be used for BigQuery access.

    Next, it binds three different roles to our service account: Data Viewer, Data Editor, and BigQuery User. These roles are necessary for the service account to access and manipulate data in BigQuery. The IAMBinding resources ensure the service account has the necessary permissions.

    We then create a bigqueryDataset with access controls. The dataset is configured to give the service account OWNER access, which is the highest level of privilege within the dataset. This means the service account can perform any action on the dataset including reading, writing, and managing it.

    Finally, we export the service account's email and dataset ID as stack outputs. These could be used in other parts of your system or for programmatic access to the service account or dataset.

    This program provides a foundation for setting up autonomous access controls in BigQuery using Pulumi. Adjust the roles and access lists as per your specific requirements. To run this program, you'll need Pulumi installed and configured with GCP credentials.