Overview of Version Control
Version control is a system that tracks changes to files over time, enabling collaboration, history tracking, and efficient project management. It allows multiple contributors to work on the same project without overwriting each other’s work and provides a rollback mechanism in case of mistakes.
Why Version Control Matters for Data Engineers
- Collaboration: Enables multiple data engineers to work on the same project without conflicts.
- Tracking Changes: Keeps a history of modifications to scripts, ETL workflows, and infrastructure code.
- Rollback & Recovery: Restores previous versions of code if something breaks.
- Code Review & Auditing: Provides transparency and accountability for changes made in a data pipeline.
- Integration with CI/CD: Automates deployment of data workflows.
Types of Version Control Systems
- Local Version Control – Simple file backups with different versions stored manually.
- Centralized Version Control (CVCS) – A single central repository used by all team members (e.g., SVN, Perforce).
- Distributed Version Control (DVCS) – Each user has a full copy of the repository, enabling offline work and robust collaboration (e.g., Git, Mercurial).
Why Git?
Git is the most widely used version control system due to its flexibility, performance, and strong branching capabilities. It is ideal for managing complex data workflows, integrating with CI/CD pipelines, and enabling efficient team collaboration.
- Benefits of Git in data workflows
- Real-world use cases for data engineers