BigQuery Analytics Hub for Secure Data Sharing

Question

Pulumi · Accepted Answer

BigQuery Analytics Hub allows organizations to create, manage, and subscribe to data exchanges and listings within Google Cloud. It provides a central place where data can be shared securely and efficiently across different teams and organizations.

In this program, we're going to define a BigQuery Analytics Hub using Pulumi's `gcp` package. A Data Exchange will be set up within a specific location, like `us` or `eu`, after which a Listing is created to allow data sharing. You can control access to this Data Exchange and Listings using IAM Members and policies, which define who can manage or view the data shared within the Data Exchange.

Here's the breakdown of tasks our program will do:
1. Create a BigQuery Analytics Hub `DataExchange`.
2. Create a BigQuery Analytics Hub `Listing`.
3. Assign IAM Members to the `DataExchange` and `Listing` for access control.

Below is a complete Pulumi program for setting up a BigQuery Analytics Hub for secure data sharing on Google Cloud:

```python
import pulumi
import pulumi_gcp as gcp

# Define the configuration for your project and location
project = "your-gcp-project-id" # replace with your GCP project ID
location = "us" # replace with your desired location

# Create a new Data Exchange within BigQuery Analytics Hub
data_exchange = gcp.bigqueryanalyticshub.DataExchange("myDataExchange",
    project=project,
    location=location,
    data_exchange_id="my-data-exchange", # Unique identifier for the data exchange
    display_name="My Data Exchange",
    description="Data exchange for sharing analytics datasets",
    # Additional optional properties can be set here if required like icon, documentation etc.
)

# Create a listing for the Data Exchange
listing = gcp.bigqueryanalyticshub.Listing("myListing",
    project=project,
    location=location,
    data_exchange_id=data_exchange.data_exchange_id,
    listing_id="my-listing", # Unique identifier for the listing
    display_name="My Listing",
    description="A listing within my data exchange",
    bigquery_dataset=gcp.bigqueryanalyticshub.ListingBigqueryDatasetArgs(
        dataset="your_dataset_id" # replace with the BigQuery dataset ID you want to share
    ),
    # Additional optional properties can be set here if required like categories, documentation, etc.
)

# Set an IAM policy for the Data Exchange to manage access control
data_exchange_iam_policy = gcp.bigqueryanalyticshub.DataExchangeIamPolicy("myDataExchangeIamPolicy",
    project=project,
    location=location,
    data_exchange_id=data_exchange.data_exchange_id,
    policy_data=pulumi.Output.secret("""{
        "bindings": [{
            "role": "roles/bigquery.dataEditor", 
            "members": ["user:user@example.com"] # replace with actual member email
        }]
    }"""),
)

# Output the Data Exchange URL for easy access to its web interface
pulumi.export("data_exchange_url", pulumi.Output.concat("https://console.cloud.google.com/bigquery/data-exchanges/", location, "/", project, "/data-exchanges/details/", data_exchange.data_exchange_id, "/overview"))

```

In the program above:
- We import `pulumi` and `pulumi_gcp` to work with Pulumi and Google Cloud resources.
- We define the project ID and location to specify the context in which we're creating our resources.
- We instantiate the `DataExchange` object, which will provision a new Data Exchange in BigQuery Analytics Hub.
- We create a `Listing` that describes the specific dataset available in our Data Exchange for others to access.
- We set IAM policies on the Data Exchange using `DataExchangeIamPolicy`, which controls who can access and edit the data.
- Finally, we use `pulumi.export` to output the URL of the created Data Exchange for easy web access. The `pulumi.Output.concat` is used to construct the URL from different pieces including the project, location, and data exchange ID.

Please ensure that you replace the placeholder values (like `your-gcp-project-id`, `your_dataset_id`, and `user@example.com`) with actual values from your environment before running this code.

This Pulumi program needs to be run in an environment where the Pulumi CLI is authenticated with Google Cloud, and the `pulumi_gcp` package is installed. The resources will be created in the Google Cloud project specified in the configuration. Access to the Data Exchange can be managed by modifying the IAM policy.