Data Marketplace Publishing with BigQuery Analytics Hub

Question

Pulumi · Accepted Answer

Publishing datasets to a data marketplace using Google's BigQuery Analytics Hub involves creating a data exchange, to which datasets are added as listings. Companies and researchers can benefit from the shared analytics without exposing the raw data. Let's create Pulumi code that sets up a data marketplace on Google Cloud Platform (GCP) using BigQuery Analytics Hub.

In this code, we'll use `gcp.bigqueryanalyticshub.DataExchange` to create a data exchange. A data exchange is a logical grouping of datasets shared by data providers. We will then use `gcp.bigqueryanalyticshub.Listing` to publish a dataset within this exchange for others to discover and use.

The following Pulumi program in Python:

1. Creates a new data exchange with Google's BigQuery Analytics Hub.
2. Creates a listing within the data exchange that references an existing BigQuery dataset.
3. Assigns the appropriate IAM roles to allow controlled access to the published data.

Before running this program, ensure you've set up Pulumi with GCP configuration, with permissions to manage BigQuery Analytics Hub resources.

```python
import pulumi
import pulumi_gcp as gcp

# Create a new Data Exchange within the BigQuery Analytics Hub.
data_exchange = gcp.bigqueryanalyticshub.DataExchange(
    "my-data-exchange",
    project="your-gcp-project-id",
    location="us",  # update the location if necessary
    data_exchange_id="my-data-exchange-id",  # Unique ID for the data exchange
    display_name="My Data Exchange",
    description="A data exchange for sharing datasets."
)

# Create a listing for the BigQuery dataset within the Data Exchange.
listing = gcp.bigqueryanalyticshub.Listing(
    "my-dataset-listing",
    project=data_exchange.project,
    location=data_exchange.location,
    data_exchange_id=data_exchange.data_exchange_id,
    listing_id="my-dataset-listing-id",  # Unique ID for the listing
    display_name="My Shared Dataset",
    description="This dataset contains valuable insights.",
    bigquery_dataset={
        "dataset": "your-bigquery-dataset-id"  # Existing BigQuery dataset ID
    }
)

# Export the Data Exchange ID and Listing ID for reference.
pulumi.export('data_exchange_id', data_exchange.data_exchange_id)
pulumi.export('listing_id', listing.listing_id)
```

Here's a breakdown of what we did in this program:

- We defined `data_exchange`, a resource representing a BigQuery Analytics Hub data exchange. This can serve as a marketplace where multiple listings (datasets) could be published.
- We created `listing`, a resource representing a published dataset within the data exchange. The reference to an existing BigQuery dataset is indicated by the `bigquery_dataset` argument.

When the Pulumi program is executed, it will create these resources in your GCP project. Data consumers can then be granted access to specific listings without you needing to share the underlying datasets directly, preserving security and control over your data.

If you need to share the created resources with others and need to ensure that the access is controlled, you would use IAM bindings. This can be done using resources like `gcp.bigqueryanalyticshub.DataExchangeIamBinding`, `gcp.bigqueryanalyticshub.ListingIamBinding`, etc., to grant specific roles to users, groups, or service accounts.

To add an IAM binding for a user or service account to a data exchange, the respective `***IamBinding` resource will be defined in the Pulumi program to manage access policies, specifying the roles and members required.

Please replace `"your-gcp-project-id"`, `"your-bigquery-dataset-id"`, and all other placeholder values with your actual project and dataset details before running the program.