Data Version Control
The Ultimate Solution for Data Version Control at Scale
Datapot transforms traditional version control into a powerful tool for data-intensive industries.
Advanced version control features
Datapot’s advanced version control features, including branching, committing, and merging are engineered to handle large repositories seamlessly. Users can create independent versions of data for different activities, track every change systematically, and seamlessly merge versions. These features ensure that no data is overwritten, providing full traceability and accountability for all changes.
Commit.
Branch.
Merge.
Big data? No problem.
Unlike other version control systems that slow down as data scales, Datapot maintains ultra-fast performance even when working with massive datasets such as terabytes of data, millions of video files, or extensive AI model training data.
Efficient data control at scale
Unlike version control systems like Git, Datapot’s server-side versioning allows users to download and work on only the necessary files instead of having to download entire repositories which is often impractical for large repositories. Users can efficiently access and manage data through a user-friendly web application, command-line interface (CLI), or by mounting repositories as drives or folders.
Adopting established software development practices
Version control has long been a cornerstone of efficient collaboration in software development teams, and now Datapot brings these established best practices to industries that deal with large-scale unstructured data. Datapot adopts these practices, so teams can collaborate effectively and consistently across all projects—whether in AI research, digital media, or other data-heavy industries.
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Data Version Control
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Data Transfer
Datapot Query Language (DQL)
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©2025 AItive Data GmbH. All rights reserved.
©2025 AItive Data GmbH. All rights reserved.
