What is DVC?

Data Version Control (DVC) is an open-source tool that helps you keep track of data files, machine-learning models, and code together, just like Git does for source code. It lets you store large files outside your Git repository while still recording which version of the data was used for each experiment.

Let's break it down

  • Data Version Control (DVC): a name that tells you the tool is for versioning (saving changes of) data.
  • Open-source: the software’s source code is free for anyone to see, use, and modify.
  • Track data files and models: it records where your data and trained models are, what their contents are, and how they change over time.
  • Like Git for code: Git is a system that saves snapshots of code; DVC does the same for data and models.
  • Store large files outside Git: Git isn’t good at handling huge files, so DVC keeps them in separate storage (cloud, local disk, etc.) but still links them to your Git history.
  • Experiment reproducibility: by knowing exactly which data and model version was used, you can rerun an experiment and get the same results.

Why does it matter?

Without DVC, data scientists often lose track of which dataset version produced a particular model, making experiments hard to reproduce and collaborate on. DVC brings order, transparency, and safety to the messy world of large data files, saving time and preventing costly mistakes.

Where is it used?

  • Machine-learning research labs: teams use DVC to manage training data, feature sets, and model checkpoints across many experiments.
  • Production AI pipelines: companies integrate DVC to version data that feeds live recommendation or fraud-detection systems, ensuring rollbacks are possible.
  • Academic projects: researchers share reproducible experiments by publishing DVC-tracked datasets alongside code.
  • Data-driven startups: small teams rely on DVC to keep their Git repo lightweight while still versioning terabytes of image or sensor data.

Good things about it

  • Works with existing Git workflows, so you don’t need to learn a completely new system.
  • Handles very large files efficiently by storing them in remote storage (S3, GCS, Azure, etc.).
  • Enables reproducible experiments: every run is linked to exact data and model versions.
  • Supports pipelines, letting you define steps and automatically track inputs/outputs.
  • Free and open-source, with an active community and many tutorials.

Not-so-good things

  • Requires extra setup and learning (remote storage config, .dvc files) beyond plain Git.
  • Performance can be slower for very large repositories if remote storage is not optimally configured.
  • Some advanced features (e.g., UI, enterprise support) are only available in paid extensions.
  • Integration with non-Python tools may need custom scripts, as the core ecosystem is Python-centric.