Day 11: Migrating Large Datasets from Git to DVC

📰 Medium · DevOps

Learn to migrate large datasets from Git to DVC to improve version control and collaboration in ML projects

intermediate Published 23 May 2026

Action Steps

Initialize DVC in your project using 'dvc init'
Track your dataset with 'dvc add'
Commit changes to DVC with 'git add' and 'git commit'
Push changes to remote repository with 'dvc push' and 'git push'
Verify dataset migration by checking DVC remote storage

Who Needs to Know This

Data scientists and engineers working on ML projects can benefit from this tutorial to manage large datasets efficiently

Key Insight

💡 DVC is designed to handle large binary files and datasets, making it a better choice than Git for version control of ML project data

Key Takeaways

Learn to migrate large datasets from Git to DVC to improve version control and collaboration in ML projects

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Title: Day 11: Migrating Large Datasets from Git to DVC

URL Source: https://medium.com/@abdullahbinaminmeo/day-11-migrating-large-datasets-from-git-to-dvc-db5410b6fed9?source=rss------devops-5

Published Time: 2026-05-23T08:01:28Z

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# Day 11: Migrating Large Datasets from Git to DVC

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May 23, 2026

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Welcome back to the Day 11 of **100 Days of MLOps** challenge!

If you work on an ML team long enough, this exact scenario will happen: a teammate, perhaps new to the workflow, accidentally commits a massive dataset directly to Git. Git is an incredible tool for source code, but it is notoriously bad at handling large binary files or massive CSVs. It bloats the repository, slows down cloning, and can eventually crash your version control system.

Yesterday, we initialized **DVC (Data Version Control)**. Today, we pl

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Day 11: Migrating Large Datasets from Git to DVC

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