12 Data Versioning Tools That Git Can’t Handle

2026-03-01 · Source: Data Engineering on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

Git is unsuitable for versioning large data files, such as 500MB Parquet files, leading to repository bloat, slow clone operations, and difficulties tracking data provenance. While Git LFS (Large File Storage) offers some relief by storing pointers in Git and large files externally, it introduces complexity with special commands and potential bandwidth costs. The core issue arises when machine learning engineers need to link specific model versions, like model v2.3, to the exact dataset version used for training, a task Git struggles with due to its design for small, line-by-line text changes rather than large binary data. This challenge highlights the need for specialized data versioning tools.

Key takeaway

For MLOps engineers and data scientists struggling with repository bloat and data provenance, relying solely on Git for datasets and models is inefficient. You should investigate dedicated data versioning tools to manage large binary files, track dataset versions, and accurately link them to specific model iterations. This will streamline your workflows and ensure reproducibility.

Key insights

Git is inefficient for versioning large datasets and models, necessitating specialized data versioning tools.

Principles

• Code belongs in Git, data does not.
• Data versioning requires specific tools.

In practice

• Avoid committing large binary files to Git.
• Use Git LFS for large files as a temporary measure.

Topics

• Data Versioning
• Git LFS
• ML Datasets
• Data Lineage
• Data Pipelines

Best for: Data Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.