DVC vs Mercurial: What are the differences?

Introduction

In this Markdown code, we will discuss the key differences between DVC (Data Version Control) and Mercurial, two popular version control systems. The differences between them are highlighted below.

1. Integration with Data Science Workflow: DVC is specifically designed for data scientists and machine learning engineers, providing seamless integration with common data science tools such as Git, Jupyter notebooks, and cloud storage platforms. On the other hand, Mercurial is a general-purpose distributed version control system suitable for various types of projects, not specifically tailored for data science workflows.

2. Data Versioning: DVC focuses on version control of large datasets and models, allowing efficient storage and management of data files in remote storage systems like AWS S3, Google Cloud Storage, and more. It provides an explicit and efficient way to version, share and collaborate on large-scale data projects. Mercurial, on the other hand, primarily focuses on version control of source code files and does not provide built-in features for large-scale data versioning.

3. Workflow Automation: DVC offers advanced capabilities for automated workflows and reproducibility of experiments. It provides a DAG (Directed Acyclic Graph) visualization that enables data scientists to track dependencies and reproduce complex data pipelines easily. Mercurial, on the other hand, does not provide specific features for workflow automation and reproducibility.

4. Branching and Merging: Both DVC and Mercurial support branching and merging operations. However, the approach and scope differ. In DVC, branching and merging are focused on managing changes in data artifacts, allowing users to create, switch between, and merge data branches efficiently. In contrast, Mercurial's branching and merging capabilities are mainly designed for source code management, allowing developers to create, switch between, and merge branches of code.

5. Collaboration and Remote Work: Mercurial has been used for many years in various open-source projects, making it widespread and well-documented. It provides extensive support for collaboration, code reviews, and remote work scenarios. DVC, although gaining popularity in the data science community, is relatively newer and may have fewer resources and established practices for collaboration and remote work.

6. Community Support and Ecosystem: Mercurial has a large community of users and developers, resulting in a rich ecosystem of plugins, extensions, and integrations with other tools. It has been extensively tested and used in a wide range of projects. DVC, being more focused on data science workflows, has a smaller but growing community and ecosystem. While DVC integrates well with common data science tools, it may have limited support for non-data-science-specific use cases.

In summary, DVC and Mercurial differ in their focus on data science workflows, data versioning capabilities, workflow automation features, collaboration and remote work support, as well as the size and maturity of their respective communities and ecosystems.