Branch, Test, Deploy: A Git-Inspired Approach for Data

Explores applying Git-style workflows to data management, addressing challenges like testing on production data and rolling back corrupted pipelines. Examines architectural approaches including zero-copy cloning, metadata-based versioning, and branching strategies that enable instant dataset duplication without physical data movement. Covers the efficiency spectrum from metadata pointers to full copies, technical implementations like Prolly Trees, and how tools leverage open table formats for version control. Promises a follow-up examining specific tools like LakeFS, Dolt, Nessie, and MotherDuck's implementations.