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

7 days ago

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Git-like workflows for data promise to solve issues like rolling back corrupted production data and testing transformations on real data without full environment setups.
Core challenges include managing local, test, and production environments efficiently, avoiding costly and time-consuming data duplication.
Git for data aims to provide peace of mind by enabling consistent rollbacks and isolated testing environments across the entire data stack.
Traditional Git isn't suitable for data due to limitations like line-level conflict resolution, lack of schema awareness, and file size constraints.
Current solutions like LakeFS, Nessie, and Dolt offer Git-like functionalities optimized for data, leveraging metadata pointers and zero-copy cloning.
Key architectural concepts include zero-copy cloning, branching via metadata catalogs, and efficient data structures like Prolly Trees.
Data movement efficiency ranges from metadata-based versioning (most efficient) to full data copying (least efficient).
Hybrid approaches combine techniques like open table formats with tools like lakeFS for comprehensive versioning and isolation.
Git for data is more complex than for code due to the need to manage state and ensure consistency across large datasets.
Future advancements in Git-like workflows for data could revolutionize testing, change management, and development velocity in data engineering.

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