Data Pipelines for GCP AI Platform Using Dataform

Question

Pulumi · Accepted Answer

Creating data pipelines is a common task in cloud environments, particularly when working with AI and data analytics workloads. Google Cloud Platform (GCP) offers various services that enable you to construct data pipelines. One such service that is particularly useful for working with structured data is Dataform, an integrated development environment (IDE) for managing data pipelines in BigQuery using SQL.

To build data pipelines in GCP specifically for the AI Platform using Dataform, we would focus on setting up and configuring the necessary GCP resources. The Dataform web application runs within the GCP project, and it establishes a connection to BigQuery to allow for the creation and management of data transformation jobs.

In this Pulumi program, we will set up a GCP project, enable the required APIs (e.g., BigQuery API, Dataform API), and start by setting up Dataform within the project. We will configure a simple transformation job to showcase how you could get started. Pulumi allows us to define our infrastructure as code, which ensures that our environment is reproducible, version controlled, and maintainable.

First, let's write a Python program using Pulumi to set up the necessary infrastructure for our data pipeline on GCP with Dataform. Make sure you have the GCP provider configured with the necessary access rights. You can do this by running `gcloud auth application-default login` if you have the Google Cloud SDK installed.

Let's begin by setting up a new project and enabling the APIs:

```python
import pulumi
import pulumi_gcp as gcp

# Replace with your GCP project ID and region.
PROJECT_ID = 'your-gcp-project-id'
REGION = 'your-gcp-region'

# Create a new GCP project
project = gcp.organizations.Project('DataformProject', project_id=PROJECT_ID)

# Enable the BigQuery API for the project.
bigquery_api = gcp.projects.Service('BigQueryApi',
    service='bigquery.googleapis.com',
    disable_on_destroy=False,
    project=project.project_id)

# Enable the Dataform API for the project.
dataform_api = gcp.projects.Service('DataformApi',
    service='dataform.googleapis.com',
    disable_on_destroy=False,
    project=project.project_id)

# Output the created project ID.
pulumi.export('project_id', project.project_id)
```

In the above code, we instantiated a new GCP project and enabled both the BigQuery API and the Dataform API, which are necessary for operating Dataform and managing BigQuery resources.

Next, we need to create an instance of Dataform in our GCP project, and define the data transformation jobs:

```python
# Continue from the previous code

# Create a Dataform instance
dataform_instance = gcp.datafusion.Instance('DataformInstance',
    location=REGION,
    type='BASIC',                 # Choose the appropriate type for your needs.
    project=project.project_id)

# Output the created Dataform instance name.
pulumi.export('dataform_instance', dataform_instance.name)
```

The `gcp.datafusion.Instance` resource initializes an instance of Dataform within our GCP project. We're specifying the location and the type of Dataform instance we would like to create - in this case, 'BASIC'.

With this setup, you would have the skeleton of a data pipeline on GCP's AI platform using Dataform. It's important to note that managing the transformation jobs within Dataform typically involves writing SQL-based scripts and organizing workflows; these tasks are executed within the Dataform IDE, not through Pulumi. Pulumi is used here to provision and manage the infrastructure that Dataform runs on, including associated services like BigQuery.

Finally, export the region and GCP project as stack outputs so they can be easily retrieved when needed. Stack outputs are key-value pairs that you can use to export the state of your stack outside of Pulumi:

```python
# Continue from the previous code

# Export the region and project id as stack outputs (e.g., to use in CI/CD or further automation).
pulumi.export('region', REGION)
pulumi.export('gcp_project', project.project_id)
```

Remember to replace `'your-gcp-project-id'` and `'your-gcp-region'` with your actual project ID and preferred region. This Pulumi program creates the infrastructural groundwork upon which you can build out your data transformation jobs using Dataform.

To run this Pulumi program, you would save this into a file like `__main__.py`, ensure you have the correct Pulumi stack selected, and use the Pulumi CLI to apply the changes with `pulumi up`. This would create the resources in your GCP environment, and outputs would give you the necessary information to check on these resources in the GCP console.