The term "data versioning" refers to the systematic management of changes to data over its lifecycle. Different versions of datasets are stored to ensure traceability, reproducibility, and transparency. Data versioning is especially important in data-intensive domains such as data science, machine learning, software development, or document management. It allows users to restore previous data states, compare changes, or analyze data evolution over time.
Version History: Logging of all changes to a dataset, including timestamp, author, and change description.
Version Comparison (Diff Function): Side-by-side display of two data versions to identify differences.
Version Restoration: Option to roll back or reactivate older data states as needed.
Versioning of Structured and Unstructured Data: Support for files, databases, or document-based data formats.
Branching and Merging: Parallel editing of data in separate paths with later merging.
Audit Logs: Transparent change logs for revision and compliance purposes.
Access Control: Role-based permissions for editing or restoring data versions.
A data science team stores multiple versions of a training dataset to compare models based on reproducible data.
In a document management system, each change to a contract is saved as a new version with a comment.
A development team performs a database migration and retains older versions of records for emergency access.
A mechanical engineering company tracks all changes to CAD files of its products across multiple versions.