Data versioning is a process in data management that allows users to save, retrieve, and explore different versions of a dataset. It is a mechanism to track changes made to data over time, preserve its history, and facilitate easy recovery of any specific versions in case of errors or other needs. It is similar to version control systems in software development but is specifically focused on handling data entities.
How Data Versioning works
In a typical data versioning workflow, when a user makes changes to a dataset, the system automatically creates a new version, without deleting or overwriting the previous one. This newly created version is then linked to its predecessor, creating a chain of versions tracing back to the original one.
When users want to retrieve a specific version of the dataset, they use the unique identifier associated with that version. The system then uses this identifier to locate and retrieve the desired version.
This ability to track and revisit data's past versions facilitates transparency, accountability and reproducibility, which are crucial in data analysis and data science workflows. It can help in understanding the evolution of data, tracking back errors, auditing data changes, and dealing with regulatory compliance.
Most modern data versioning systems also provide functionalities to compare different versions, merge changes, handle conflicts, and even collaborate with others in a controlled manner. Some systems also support versioning of data transformations and machine learning models, allowing a complete version-controlled data science workflow.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.
Several people are typing about AI/ML security. Come join us and 1000+ others in a chat that’s thoroughly SFW.