Designing a Docker-Powered Transform Command with Auto-Versioning in Python

In my previous post, I wrote about DataTracker's storage architecture (hashes, objects, and SQLite metadata). This follow-up is about what I think is the most technically interesting command: transform . If you have not read the first post, quick context: DataTracker is a local CLI tool for versioning datasets (files or directories) with git-like commands ( add , update , history , compare , diff , export , etc.). This article focuses on one question: How do you run a data transformation in Docker and still keep version history useful instead of chaotic? Why transform Exists at All Most data versioning tools stop at "store versions". That is useful, but in real workflows the interesting part is what happens between versions: cleaning reshaping converting formats running scripts in reproducible environments I wanted this to be one command, not three manual steps each time. Without dt transform , the flow looks like this: Run some custom Docker command manually Hope the output is written