Cross-Project BigQuery Data Transfers for Federated ML Datasets

Question

Pulumi · Accepted Answer

To perform cross-project BigQuery data transfers for federated machine learning datasets, we need to create a data transfer configuration in Google Cloud BigQuery that specifies the source and destination datasets, the transfer schedule, and various other settings pertinent to the data transfer.

In this use case, we will be making use of the following Pulumi resources:

1. `gcp.bigquery.Dataset`: Represents a BigQuery dataset within which our tables and data reside. We will need to create this resource for both the source and destination projects.
2. `gcp.bigquery.DataTransferConfig`: This will define the configuration for transferring data from the source dataset to the destination dataset on a specified schedule.

Here's a step-by-step approach to setting up cross-project BigQuery data transfers using Pulumi:

### Step 1: Import the necessary modules
Before we start, it is important to import the required modules. This includes `pulumi`, `pulumi_gcp` which gives us access to the resources we need to configure BigQuery datasets and data transfer configurations.

### Step 2: Define the datasets
We will define both the source and destination datasets using `gcp.bigquery.Dataset`. Each dataset will be created within its respective Google Cloud Project and will contain the necessary configurations such as access permissions, location, and dataset ID.

### Step 3: Set up the data transfer configuration
We will use `gcp.bigquery.DataTransferConfig` to set up the data transfer. This will define the dataset IDs, project IDs, and the data transfer schedule. We will also specify any other necessary parameters required for our federated ML datasets.

### Step 4: Export relevant outputs
Finally, we will use `pulumi.export` to export any relevant outputs, such as the destination dataset ID and data transfer configuration ID. This is helpful for integrating our Pulumi stack with other processes or for reference.

Now let's write the Pulumi program in Python:

```python
import pulumi
import pulumi_gcp as gcp

# Replace these values with the actual project and dataset IDs
source_project_id = "source-project-id"
source_dataset_id = "source-dataset-id"
destination_project_id = "destination-project-id"
destination_dataset_id = "destination-dataset-id"

# Source dataset in the source project
source_dataset = gcp.bigquery.Dataset("sourceDataset",
    dataset_id=source_dataset_id,
    project=source_project_id,
    location="US"
)

# Destination dataset in the destination project
destination_dataset = gcp.bigquery.Dataset("destinationDataset",
    dataset_id=destination_dataset_id,
    project=destination_project_id,
    location="US"
)

# Data transfer configuration
data_transfer_config = gcp.bigquery.DataTransferConfig("dataTransferConfig",
    destination_dataset_id=destination_dataset.dataset_id,
    display_name="Federated ML Dataset Transfer",
    data_source_id="cross_region_copy",
    params={
        "source_project_id": source_project_id,
        "source_dataset_id": source_dataset.dataset_id,
    },
    schedule="every 24 hours",
    project=destination_project_id
)

# Export the destination dataset and transfer config IDs
pulumi.export("source_dataset_id", source_dataset.dataset_id)
pulumi.export("destination_dataset_id", destination_dataset.dataset_id)
pulumi.export("data_transfer_config_id", data_transfer_config.id)
```

This script sets up the necessary resources for transferring BigQuery data across different projects, which is a common requirement for federated machine learning datasets where data collaboration between different organizations or departments is required.

Remember to replace the placeholder values in `source_project_id`, `source_dataset_id`, `destination_project_id`, and `destination_dataset_id` with your actual Google Cloud project and dataset IDs.

Run this code using the Pulumi CLI with the following commands:

```bash
pulumi up # To preview and deploy the changes
```

This will apply the configuration and provision the resources as defined in the script. It will also give you an output with the created resource IDs which you can reference later if needed.