Real-time, incremental ETL library for Python with record-level dependency tracking

Currently in alfa. Join the waitlist for detailed documentation and usage cases.
Снимок экрана 2023 09 26 в 13.54.45


  • Incremental processing

    Datapipe processes only new or modified data, significantly reducing computation time and resource usage.

  • Real-time

    The library supports real-time data extraction, transformation, and loading.

  • Dependancy tracking

    Automatic tracking of data dependencies and processing states.

  • Python integration

    Seamlessly integrates with Python applications, offering a Pythonic way to describe data pipelines.

Datapipe lets you build complex pipelines and track data dependencies. We can trace how a specific data row evolves from the first to the last transformation.

Youtube video gif (2)

Datapipe early adopters

  • Projects with complex ML pipelines with a human-in-the-loop component
  • ML projects that require real-time model retraining based on newly labeled data
  • Projects that require content moderation


Hear from our satisfied users.

Imagine you have an ML pipeline with detection and classification steps. As development progresses, you eventually have 100 detection and 100 classification models to apply to a dataset of 1000 pictures. If you run the entire processing for the first time, it would take a few days. However, if you add more images or models later, only the new data will be incrementally recalculated, eliminating the need to write any additional code.

Renat Shakirov, ML engineer

The integration of human input into ML models through Datapipe is extremely efficient. Annotators' input is seamlessly incorporated into the pipeline, enabling real-time retraining of all models. This allows annotators to observe immediate improvements, thus accelerating their work.

Arseniy Koryagin, ml team lead

The ability to run pipelines in different modes – full or partial calculation – is invaluable. For real-time predictions and quick integration of new data, you can run the pipeline with a subset of data, ensuring agility and efficiency.

Renat Shakirov, ML engineer

Datapipe allows room for errors. If you miss a case in a transformation and it crashes, you can quickly fix it and resume the calculation without having to start all over again.

Andrey Serov, lead engineer

When you delete something in Datapipe, the cleanup is automatic and thorough. It saves a lot of time and ensures data integrity.

Aleksander Filatov, data analyst

Contact Us

Have questions? We're here to help.
  • 1588250735582
    Andrey Tatarinov

    Andrey is the founder of – agency for AI/ML projects. Developed Datapipe to speed up client ML projects

  • Screenshot 2024 01 26 at 17.45.52
    Alexander Kozlov
    ML developer

    A pioneering early adopter of Datapipe. He has developed over 1000 pipeline nodes across numerous client projects

  • 3xlylmez4w8
    Andrey Serov
    Lead Engineer

    Initially Andrey worked for our first client to implement Datapipe. He fixed numerous issues in the Datapipe core and eventually joined our team.

  • Screenshot 2024 01 26 at 17.46.02
    Olga Tatarinova

    Had to learn Python coding to work on Datapipe.

Stay Connected

Subscribe to our newsletter for updates and news.
Error. Your form has not been submittedEmoji
This is what the server says:
There must be an @ at the beginning.
I will retry