Data Version Control & Reproducibility

Version control means keeping a tracked history of changes so you can see what changed, when, and by whom — and roll back if needed. Code has Git for this; data and models need extra tooling (like DVC or lakehouse table formats) because datasets are large and change constantly. Reproducibility is the goal it serves: anyone, including future-you, should be able to re-run an analysis and get the same result.

This matters because an analysis you cannot reproduce is hard to trust, debug, or build on. The trap that beginners hit is a result that depends on a specific version of the data plus specific code plus a specific environment — change any one silently and the numbers move. Solid practice pins all three together: version the code in Git, version (or snapshot) the data, and record the environment, so a result can always be traced back to the exact inputs that produced it.