DataOps 101

Infrastructure as Code in Data Engineering

Imagine manually setting up and managing your cloud infrastructure every time you deploy a new feature/data pipeline to collect its data. The risk of misconfigurations, inconsistent environments, and wasted time is high. That’s where Infrastructure as Code (IaC) comes in. Without IaC, you're leaving too much room for error and with it, you can:

• automate the creation, management, and scaling of cloud resources
• ensure everything is reproducible, consistent, and version-controlled.

Why Terraform Isn’t Enough Alone?
Terraform excels at provisioning cloud infrastructure, but as environments grow in complexity, managing them with plain Terraform becomes challenging.

Without Terragrunt, you risk duplicating code across environments (dev, staging, production), leading to maintenance headaches and reduced flexibility. Terragrunt extends Terraform’s capabilities by enforcing a DRY (Don’t Repeat Yourself) approach.

It simplifies environment management, keeps configurations clean and consistent, and allows you to reuse Terraform modules for new pipelines—only changing variables instead of recreating services from scratch.

What You’ll Build Next?
In the next post, we’ll dive into a hands-on exercise to build a data engineering pipeline using Terraform and Terragrunt. Here’s what we’ll provision:

Google Cloud Pub/Sub for event streaming.
BigQuery tables and datasets for data storage.
Policy Tags to manage sensitive data like PII.
dbt for data transformation and modeling.
Airflow (GCP Composer) for workflow orchestration and scheduling.

You’ll use Terraform to define GCP resources and Terragrunt to manage environment-specific configurations, ensuring your pipeline is automated, scalable, and consistent across environments.

For more context, check out this short article on data mesh by Atlan.