How should I set up my source control for data engineering?
Source control plays a vital role in modern data engineering services. It is more than just tracking code. It brings structure to your work, helps teams collaborate effectively and makes your projects more reliable.
With the correct setup, you can manage complex data transformation tasks, support scalable data systems architecture and improve the way you handle data modelling. It also helps reduce errors and keeps your workflow consistent across the team.
In this article, we’ll walk through some best practices to help you set up source control that fits your workflow and supports long-term success, and aligns with your broader data strategy.
What is source control and why is it important for data teams?
Source control, also known as version control, helps you track changes to your code, models, and configurations. It captures any changes, when they occurred and why, so you have a clear view at all times.
For data engineering teams, this is invaluable. It goes beyond SQL scripts and encompasses everything from pipeline logic and data transformation workflows to infrastructure and documentation. It acts as a single source of truth that empowers the team to stay aligned and collaborate more effectively.
Support data systems architecture with source control
Achieving good modern data architecture is built on clear thinking, a shared understanding and repeatable processes. Source control makes all of this possible for data engineering teams and takes the headache out of data systems architecture.
Versioning infrastructure as code tools, such as Terraform, enables teams to make changes safely and consistently. This approach is critical for building resilience in your environments, helping with disaster recovery, and allowing your infrastructure to scale your organisation’s needs with confidence.
When the whole data engineering team works from the same single source of truth, it makes projects more predictable and aligns the collective approach to ensure things run seamlessly.
Setting up your source control to support your data systems architecture helps simplify your infrastructure management and improves workflows for your data engineering teams. As AI agents produce more code, source control becomes increasingly important to track and review changes that are AI-written.
Create more structure with data modelling
Data modelling defines how your organisation understands information, removing any guesswork out of the equation. Storing these models in source control adds more structure to data modelling by allowing you to quickly and easily track, record and review your data models.
As changes are made, you can clearly see who took action and the reason why the changes happened. Data build tools make it easier to treat models as code, so you can manage them in source control with fewer mistakes and more reliable outcomes.
A well-maintained source-controlled data model allows you to see not just what changes have been made, but also the evolving thinking of the organisation’s approach to its data over time.
Ultimately, versioning your data modelling as part of a robust data management strategy helps ensure accuracy, stability and trust.
Best practices for source control in data engineering
When it comes to setting up your source control for data engineering, there are some best practices to consider. Here are some of the best practices to bring source control into your data engineering workflow:
- Use Git as your source control tool
- Ensure you organise your repositories clearly by function, team and domain
- Keep all pipeline code, models and infrastructure neatly structured
- Write commit messages to explain why changes were made, not just what changed
- Manage features, fixes and experiments with branches
- Support quality and learning by using pull requests to review changes
- Consider automating testing and deployment wherever you can
- Store documentation close to your code so it’s easy to update and access
Following these best practices for source control can help you gain a clear picture, mitigate the risk of errors or duplication and facilitate better collaborations. They can also help deliver reliable data transformation practices across your team.
Build source control into your everyday data practice
Source control helps you bring structure and speed to your data engineering teams. It gives them the tools they need to work together with confidence, Pairing this with executive leadership coaching helps to gain a shared understanding and implement smoother deployments.
Ready to get started with source control? Get in touch for an informal chat about how we can begin supporting you.
