DataChain icon
DataChain icon

DataChain

DataChain builds a suite of tools for data preprocessing and management, experiment tracking, ML models versioning, and pipeline automation.

Cost / License

  • Freemium
  • Open Source

Platforms

  • Python
  • Online
  • Software as a Service (SaaS)
  • Self-Hosted
-
No reviews
0likes
0comments
0news articles

Features

Suggest and vote on features
  1.  File Versioning
  2.  Python-based
  3.  Data-management
  4.  Data analytics
  5.  Pipeline Management
  6.  Data enrichment

 Tags

  • data-versioning
  • large-dataset-analysis
  • multimodal
  • etl
  • Data Analysis
  • data-preprocessing
  • unstructured-data
  • data-processing
  • datasets

DataChain News & Activities

Highlights All activities

Recent activities

Show all activities

DataChain information

  • Developed by

    US flagDataChain, Inc.
  • Licensing

    Open Source (Apache-2.0) and Freemium product.
  • Pricing

    free version with limited functionality.
  • Written in

  • Alternatives

    8 alternatives listed
  • Supported Languages

    • English

GitHub repository

  •  2,716 Stars
  •  132 Forks
  •  100 Open Issues
  •   Updated  
View on GitHub
DataChain was added to AlternativeTo by Paul on and this page was last updated .
No comments or reviews, maybe you want to be first?
Post comment/review

What is DataChain?

The copilot for unstructured data.

Build, debug and version multimodal datasets - video, audio, images, parquet and more.

  • IDEs Powered by Data Context: Share data, data lineage and code with your IDE like Cursor and GitHub Copilot via MCP — enabling smarter code generation.
  • Pythonic stack: One language across code and data without SQL islands. Easier for developers, better for IDEs and agents.
  • IDE-Native for Cloud Scale: Build and debug datasets processing locally. Scale instantly in 100s of cloud GPUs.
  • No Data Duplication: Operate on references to data in cloud storage - no data copies, no format changes, no vendor lock-in.

See what DataChain can do

  • Master multimodal data with seamless ETL: Apply LLMs and ML models to extract insights from videos, PDFs, audio, and other unstructured data types. Effortlessly organize it into ETL processes.
  • Reproduce and data lineage: Track data lineage with all code and data dependencies. Reproduce datasets, and update them automatically via ETL.
  • Large-Scale Data Processing: Efficiently handle millions or billions of files. Leverage ML models for data filtration, join datasets seamlessly, and compute dataset updates with ease.

DataChain Videos

Official Links