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Data version control is a method of working with data sets. It is similar to the version control systems used in traditional software development, but is optimized to allow better processing of data and collaboration in the context of data analytics, research, and any other form of data analysis.
This template presents version history tables in a standardized format. Note that you may have to insert it in source mode, not visual mode. Many articles on Wikipedia use color-coded tables to illustrate the version or release history of software. The template has been imported from German Wikipedia, where it is used as the current standard for color-coding history tables. This template is ...
ML model checkpoints versioning: The new release also enables versioning of all checkpoints with corresponding code and data. Metrics logging: DVC 2.0 introduced a new open-source library DVC-Live that would provide functionality for tracking model metrics and organizing metrics in a way that DVC could visualize with navigation in Git history.
This category is for pages which detail the version history of a particular piece of software. Pages in category "Software version histories" The following 62 pages are in this category, out of 62 total.
MIL-STD-498 standard describes the development and documentation in terms of 22 Data Item Descriptions (DIDs), which were standardized documents for recording the results of each the development and support processes, for example, the Software Design Description DID was the standard format for the results of the software design process.
Version control (also known as revision control, source control, and source code management) is the software engineering practice of controlling, organizing, and tracking different versions in history of computer files; primarily source code text files, but generally any type of file. Version control is a component of software configuration ...
lakeFS is a data versioning engine that manages data in a way similar to code. By using operations such as branching , committing , merging , and reverting , which resemble those found in Git , it facilitates the handling of data and its corresponding schema throughout the entire data life cycle.
Prepare the schema so that it can hold data in both the old and new formats. This might mean adding a new version of a column or a table, without affecting existing data. Deploy a new version of the application which writes data in both the old and new formats (hence the name dual writing). It's important to ensure consistency of these writes ...