Summary (TL/DR): Pipeline code needs the same rigor as application code. Here you’ll learn how Git, branching, and peer reviews enable safe, collaborative development in data projects.
When people think of version control, they often picture software developers managing code. But in data engineering, version control is just as essential—not only for your code, but also for pipeline logic, infrastructure configurations, and data contracts.
Whether you’re building new connectors, updating schemas, or tuning a pipeline for performance, you need a system that tracks changes, supports collaboration, and helps avoid breaking production.
Why Version Control Matters
In data workflows, things change constantly—API schemas evolve, business logic shifts, and sync schedules get updated. Version control (usually with Git) provides a framework to:
- Change history – See exactly what changed, when, and why. This helps with debugging and learning from past decisions.
- Safe experimentation – Try new ideas in feature branches without affecting the main pipeline.
- Prevents conflicts – Collaborators can work in parallel and safely merge their changes through pull requests.
- Review and accountability – Every change goes through peer review, which catches issues early and promotes shared understanding.
This isn’t just about writing cleaner code—it’s about building resilient pipelines that multiple people can maintain and improve over time.
How We Used It on Our Team
In our project, we had several repositories:
- One for pipeline orchestration logic
- One for shared connector libraries
- One for infrastructure-as-code configs
We followed these collaboration patterns:
- Clear branching strategy: We used feature branches for all work, merged into main only after review and testing.
- Pull requests + code reviews: This was where most of the collaboration happened—reviewing logic, suggesting improvements, and flagging risks.
- Rebasing carefully: To keep commit history clean and avoid merge conflicts during long-running feature work.
- Tagging and releases: For stable versions of the pipeline or when shipping changes to production.
This gave us confidence in what we were shipping and made onboarding new teammates easier, since they could see the project’s full history and structure.
Visual: Branching Workflow in Practice
To support safe and collaborative development, we used a branching strategy like the one below—each feature was developed in its own branch, reviewed, and only then merged into main:
This structure helped us isolate workstreams (e.g., pagination logic, authentication fixes, schema cleanup) without blocking one another or risking unstable builds.
Version control isn’t an afterthought in data engineering—it’s a core practice that helps teams move fast without breaking things. And when combined with good collaboration habits, it turns your pipeline into a shared, maintainable system rather than a fragile set of scripts.
Next, let’s look at how we tested, debugged, and deployed those pipelines with confidence.
This article was written by Andrea Kuruppu, our superstar Data Engineer 🌟 Since we cover a lot of concepts here, we divided it into parts:
- Part I: The basics – Fetching the Data
- Part II: Data Processing and Pipeline Optimization
- Part III: Security – Managing Secrets and Authentication with a Proxy API
- Part IV: Version Control and Collaboration
- Part V: Testing, Debugging, and Deployment
- Part VI: Closing Thoughts – Challenges and Best Practices
- Part VII: Summary
