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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.
Pros of DVC
- Full reproducibility2
Pros of Git
- Distributed version control system1.4K
- Efficient branching and merging1.1K
- Fast959
- Open source845
- Better than svn726
- Great command-line application368
- Simple306
- Free291
- Easy to use232
- Does not require server222
- Distributed28
- Small & Fast23
- Feature based workflow18
- Staging Area15
- Most wide-spread VSC13
- Disposable Experimentation11
- Role-based codelines11
- Frictionless Context Switching7
- Data Assurance6
- Efficient5
- Just awesome4
- Easy branching and merging3
- Github integration3
- Compatible2
- Possible to lose history and commits2
- Flexible2
- Team Integration1
- Easy1
- Light1
- Fast, scalable, distributed revision control system1
- Rebase supported natively; reflog; access to plumbing1
- Flexible, easy, Safe, and fast1
- CLI is great, but the GUI tools are awesome1
- It's what you do1
- Phinx0
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Cons of DVC
- Coupling between orchestration and version control1
- Requires working locally with the data1
- Doesn't scale for big data1
Cons of Git
- Hard to learn16
- Inconsistent command line interface11
- Easy to lose uncommitted work9
- Worst documentation ever possibly made8
- Awful merge handling5
- Unexistent preventive security flows3
- Rebase hell3
- Ironically even die-hard supporters screw up badly2
- When --force is disabled, cannot rebase2
- Doesn't scale for big data1