DVC vs Git: What are the differences?

Differences Between DVC and Git

DVC (Data Version Control) and Git are both version control tools, but they serve different purposes and have some key differences:

1. Data vs Code:

DVC is specifically designed for version controlling data and machine learning models, whereas Git is primarily used for tracking changes in code. DVC provides a separate layer of version control for large datasets, facilitating reproducibility and collaboration in data science projects.

2. File Organization:

In Git, all files and directories are tracked as a whole, and any changes to files within a directory are treated as changes to the entire directory. On the other hand, DVC tracks individual files separately, allowing more flexibility in managing and versioning specific datasets or models.

3. File Storage:

Git stores all file versions locally on the user's machine, resulting in a large repository size for projects with numerous and large files. In contrast, DVC stores data files and models externally, reducing the repository size and enabling efficient sharing and collaboration by referencing the storage locations rather than storing the actual files.

4. Time Complexity:

When working with large datasets, Git can become slow as it needs to check the entire repository for changes during each commit. DVC, by separating data versioning from code versioning, reduces the time complexity in managing and tracking large datasets, allowing for faster commits and better performance.

5. Collaboration:

Git provides robust mechanisms for collaborative code development, such as branches, merging, and pull requests. While DVC can also facilitate collaboration by versioning data, its collaboration capabilities are more focused on facilitating the sharing and reproducibility of data and models rather than the collaborative development of code.

6. Integration:

Git seamlessly integrates with various development tools and platforms, making it widely adopted in the software development community. DVC, on the other hand, has a more specialized focus on data science workflows and integrates with popular machine learning frameworks, cloud storage providers, and ML experiment tracking tools.

In Summary, DVC and Git have key differences regarding their intended use, file organization, storage approach, time complexity, collaboration capabilities, and integration options.