Access Control for External BigQuery Dataproc Integration

Question

Pulumi · Accepted Answer

Access control in a cloud environment, such as Google Cloud Platform (GCP), is crucial for managing who has permission to interact with your services and data. In the context of GCP, BigQuery is a fully-managed data warehouse that allows you to execute SQL-like queries on large datasets, and Dataproc is a managed Apache Hadoop and Spark service for running big data workloads.

When integrating BigQuery and Dataproc, one might want to control access to BigQuery datasets from Dataproc jobs and ensure only specific users or services can access or write data. This can be achieved by setting IAM (Identity and Access Management) policies that define permissions for different resources.

In the Pulumi program below, I will show you how to use IAM bindings to control access to a BigQuery dataset and how to configure a Dataproc cluster, which might interact with BigQuery. We define the IAM roles for accessing the BigQuery dataset and specify the service account that the Dataproc cluster will use. That service account will be granted appropriate permissions to work with BigQuery.

Let's start with the program:

```python
import pulumi
import pulumi_gcp as gcp

# Define a service account for Dataproc cluster to interact with BigQuery.
dataproc_service_account = gcp.serviceaccount.Account("dataprocServiceAccount",
                                                     account_id="dataproc-service-account",
                                                     display_name="Dataproc Service Account")

# Grant the service account roles for BigQuery data editor.
# This allows the service account to run jobs in Dataproc that can manipulate data in BigQuery.
bigquery_data_editor_iam = gcp.projects.IAMMember("bigqueryDataEditorIAM",
                                                  role="roles/bigquery.dataEditor",
                                                  member=pulumi.Output.concat("serviceAccount:", dataproc_service_account.email))

# Define the BigQuery dataset that the Dataproc cluster will access.
dataset = gcp.bigquery.Dataset("myDataset",
                               dataset_id="my_dataset_id",
                               description="Dataset accessible to Dataproc")

# Define the IAM binding for the BigQuery dataset granting access to the service account.
dataset_iam_binding = gcp.bigquery.DatasetIamBinding("datasetIamBinding",
                                                     dataset_id=dataset.dataset_id,
                                                     role="roles/bigquery.dataEditor",
                                                     members=[pulumi.Output.concat("serviceAccount:", dataproc_service_account.email)])

# Define a Dataproc cluster which will interact with BigQuery.
dataproc_cluster = gcp.dataproc.Cluster("myDataprocCluster",
                                        cluster_config=gcp.dataproc.ClusterClusterConfigArgs(
                                            master_config=gcp.dataproc.ClusterClusterConfigMasterConfigArgs(
                                                num_instances=1,
                                                machine_type="n1-standard-1"
                                            ),
                                            worker_config=gcp.dataproc.ClusterClusterConfigWorkerConfigArgs(
                                                num_instances=2,
                                                machine_type="n1-standard-1"
                                            ),
                                            # Assign the service account to the Dataproc cluster
                                            gce_cluster_config=gcp.dataproc.ClusterClusterConfigGceClusterConfigArgs(
                                                service_account=dataproc_service_account.email
                                            ),
                                        ))

# Export the ids of the created resources.
pulumi.export("dataproc_cluster_id", dataproc_cluster.id)
pulumi.export("bigquery_dataset_id", dataset.dataset_id)
```

In this program:

1. We first define a GCP Service Account which will be used by our Dataproc cluster to integrate with BigQuery securely. 
2. We grant the `roles/bigquery.dataEditor` IAM role to this service account, providing it with permissions to manipulate data in BigQuery datasets.
3. We define a BigQuery dataset that will be managed and potentially accessed by the Dataproc cluster.
4. We bind the service account to the dataset using an IAM policy, which allows access control to be handled at the dataset level.
5. We create the Dataproc cluster configuration and specifically declare our service account to be used with the GCE instances that Dataproc employs. This allows jobs run on Dataproc to access the BigQuery dataset using the permissions granted to the service account.
6. We export the cluster ID and dataset ID for potential use in other parts of our Pulumi program or for reference.

By setting up these resources, you establish a connection between Dataproc and BigQuery with controlled access, ensuring that only authorized operations can be performed by the Dataproc jobs.