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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.

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Pros of DVC
Pros of Git
  • 2
    Full reproducibility
  • 1.4K
    Distributed version control system
  • 1.1K
    Efficient branching and merging
  • 959
    Fast
  • 845
    Open source
  • 726
    Better than svn
  • 368
    Great command-line application
  • 306
    Simple
  • 291
    Free
  • 232
    Easy to use
  • 222
    Does not require server
  • 27
    Distributed
  • 22
    Small & Fast
  • 18
    Feature based workflow
  • 15
    Staging Area
  • 13
    Most wide-spread VSC
  • 11
    Role-based codelines
  • 11
    Disposable Experimentation
  • 7
    Frictionless Context Switching
  • 6
    Data Assurance
  • 5
    Efficient
  • 4
    Just awesome
  • 3
    Github integration
  • 3
    Easy branching and merging
  • 2
    Compatible
  • 2
    Flexible
  • 2
    Possible to lose history and commits
  • 1
    Rebase supported natively; reflog; access to plumbing
  • 1
    Light
  • 1
    Team Integration
  • 1
    Fast, scalable, distributed revision control system
  • 1
    Easy
  • 1
    Flexible, easy, Safe, and fast
  • 1
    CLI is great, but the GUI tools are awesome
  • 1
    It's what you do
  • 0
    Phinx

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Cons of DVC
Cons of Git
  • 1
    Coupling between orchestration and version control
  • 1
    Requires working locally with the data
  • 1
    Doesn't scale for big data
  • 16
    Hard to learn
  • 11
    Inconsistent command line interface
  • 9
    Easy to lose uncommitted work
  • 7
    Worst documentation ever possibly made
  • 5
    Awful merge handling
  • 3
    Unexistent preventive security flows
  • 3
    Rebase hell
  • 2
    When --force is disabled, cannot rebase
  • 2
    Ironically even die-hard supporters screw up badly
  • 1
    Doesn't scale for big data

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What is DVC?

It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.

What is Git?

Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

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What companies use DVC?
What companies use Git?
See which teams inside your own company are using DVC or Git.
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What tools integrate with DVC?
What tools integrate with Git?

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What are some alternatives to DVC and Git?
Pachyderm
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
SVN (Subversion)
Subversion exists to be universally recognized and adopted as an open-source, centralized version control system characterized by its reliability as a safe haven for valuable data; the simplicity of its model and usage; and its ability to support the needs of a wide variety of users and projects, from individuals to large-scale enterprise operations.
Mercurial
Mercurial is dedicated to speed and efficiency with a sane user interface. It is written in Python. Mercurial's implementation and data structures are designed to be fast. You can generate diffs between revisions, or jump back in time within seconds.
Plastic SCM
Plastic SCM is a distributed version control designed for big projects. It excels on branching and merging, graphical user interfaces, and can also deal with large files and even file-locking (great for game devs). It includes "semantic" features like refactor detection to ease diffing complex refactors.
See all alternatives