CI/CD & GitOps for Data
/tldr: Automating the build, test, and deployment of data assets and infrastructure using version control.
1. GitOps: The Data Deployment Philosophy
GitOps treats Git as the single source of truth for declarative infrastructure and applications. For data, this means:
Continuous Integration (CI)
The practice of frequently merging code changes into a central repository.
- **Data Focus:** Running dbt tests (unit/schema), linting Python/SQL code, checking Airflow DAG syntax, and building temporary development environments.
- **Goal:** Catching errors early and guaranteeing the code is ready for deployment.
Continuous Delivery (CD)
Automating the process of delivering the final tested code to a production environment.
- **Data Focus:** Deploying new dbt models, updating Airflow DAGs, promoting code across dev/staging/prod environments, and applying infrastructure changes.
- **Goal:** Fast, reliable, and repeatable production releases without manual intervention.
2. Promotion Strategy (Dev → Prod)
Promotion strategies rely on isolating code and data assets until they are validated. This typically involves at least three environments:
The 3-Tier Structure
- **Development (Dev):** Where engineers write and test code. Each developer often has their own dedicated schema/database to prevent conflicts.
- **Staging/QA:** An environment that mirrors production data/scale. Used for integration testing (e.g., checking if the new dbt model breaks a downstream dashboard).
- **Production (Prod):** The live environment where jobs run on full, live data. Only fully tested and approved code should reside here.
Promotion occurs when a successful branch merge (e.g., merging the main branch into the production branch) triggers the CD pipeline to deploy assets to the next environment.
3. Tool-Specific CI/CD Integration
dbt Cloud
Offers native integration with Git repositories (GitHub/GitLab/Bitbucket).
- CI checks trigger a
dbt testrun on a specific pull request (PR). - Environments are managed via projects, allowing separate connections and credential sets for Dev/Staging/Prod targets.
Databricks Repos
Databricks' core mechanism for integrating notebooks and files with Git.
- Allows developers to work on a branch and PR their changes.
- CI/CD systems can use the **Databricks Jobs API** to execute notebooks from specific branches, ensuring the latest tested code runs in production jobs.
Airflow/Astronomer
DAGs are Python files checked into Git. Deployment means ensuring the webserver and scheduler have access to the latest code.
Managed services like Astronomer simplify this greatly by providing dedicated tools to deploy a **pre-validated** Docker image containing the DAGs and dependencies to the Airflow cluster.
4. Terraform for Infrastructure Automation
Infrastructure as Code (IaC) means defining cloud resources (like data warehouses, compute clusters, network rules) in declarative configuration files, which are then managed by GitOps principles.
Terraform Role in Data
Terraform is the industry standard for provisioning, changing, and versioning infrastructure safely and efficiently.
- **Warehouse Setup:** Creating Snowflake databases, schemas, users, and roles for Dev/Staging/Prod.
- **Platform Configuration:** Provisioning Databricks clusters, Airflow environments (MWAA/Composer/Astronomer Deployments).
- **CI/CD Pipeline:** A Terraform run is often the final step in a CD pipeline, ensuring the environments themselves are standardized across promotions.
Workflow: Developer updates .tf file in Git → PR → CI pipeline runs terraform plan (checks changes) → Merge triggers CD pipeline to run terraform apply (applies changes).
CI/CD elevates data engineering from writing individual scripts to managing a fully automated, resilient data platform.