1. Secure Multi-Tenant Data Environments with BigQuery DatasetAccess.


    When configuring a secure multi-tenant data environment within Google BigQuery, it is essential to manage access controls efficiently and effectively. BigQuery is Google Cloud's fully managed, scalable, and serverless data warehouse designed for business agility. Managing Dataset Access ensures that only the right entities (users, groups, services accounts) have the appropriate level of access to datasets, respecting tenant boundaries and data privacy.

    We aim to utilize the gcp.bigquery.DatasetAccess resource to define specific access controls for a BigQuery dataset. Access controls allow us to specify who can access the data and what level of privileges they have, such as the ability to read, write, or administer datasets.

    Here's a program written in Pulumi with Python which creates a multi-tenant environment:

    1. It sets up a new BigQuery dataset.
    2. It configures the dataset's access policy to provide fine-grained access controls suitable for a multi-tenant setup.

    This configuration might include setting up roles for views (so that users can only see certain projections of the data), different domains (to separate different organizational units or customers), groups (for managing department level access), or even specific users.

    import pulumi import pulumi_gcp as gcp # Create a new BigQuery dataset for our multi-tenant environment tenant_dataset = gcp.bigquery.Dataset("tenantDataset", dataset_id="my_dataset", friendly_name="Tenant Dataset", description="A dataset for managing multi-tenant data.", # Set appropriate location for your dataset, it impacts data residency and compliance location="US" ) # Define roles and members for our multi-tenant dataset # This example assumes we have two tenants with distinct groups. "readers" can only view data. # "admins" can manage the dataset. Adjust the groups and roles as needed for your actual tenants. # Tenant A viewers tenant_a_viewers = gcp.bigquery.DatasetAccess("tenantAViewers", dataset_id=tenant_dataset.dataset_id, role="READER", group_by_email="tenant-a-readers@your-domain.com" ) # Tenant A admins tenant_a_admins = gcp.bigquery.DatasetAccess("tenantAAdmins", dataset_id=tenant_dataset.dataset_id, role="WRITER", group_by_email="tenant-a-admins@your-domain.com" ) # Tenant B viewers tenant_b_viewers = gcp.bigquery.DatasetAccess("tenantBViewers", dataset_id=tenant_dataset.dataset_id, role="READER", group_by_email="tenant-b-readers@your-domain.com" ) # Tenant B admins tenant_b_admins = gcp.bigquery.DatasetAccess("tenantBAdmins", dataset_id=tenant_dataset.dataset_id, role="WRITER", group_by_email="tenant-b-admins@your-domain.com" ) # Output the identifiers of the dataset and its access controls pulumi.export("tenant_dataset_id", tenant_dataset.dataset_id) pulumi.export("tenant_a_viewers_id", tenant_a_viewers.id) pulumi.export("tenant_a_admins_id", tenant_a_admins.id) pulumi.export("tenant_b_viewers_id", tenant_b_viewers.id) pulumi.export("tenant_b_admins_id", tenant_b_admins.id)

    In this program, we:

    • Utilize the gcp.bigquery.Dataset resource to represent a BigQuery dataset. We pass parameters such as dataset_id, friendly_name, description, and location to the constructor, which are self-explanatory fields to create a dataset.
    • Use the gcp.bigquery.DatasetAccess resource to define access rules to the dataset. We declare multiple instances of DatasetAccess for different groups of users, and we assign roles like READER and WRITER for each group. These roles define the level of access—READER for read-only access, WRITER for read and write access.
    • Export the IDs of the datasets and their access configurations to retrieve them easily for further operations.

    This Pulumi program should run inside a directory with a Pulumi.yaml file that defines the Python project configuration. Running this program with Pulumi will perform the necessary API calls to Google Cloud to create these resources with the specified properties.