kedro open source analysis
Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Project overview
⭐ 10697 · Python · Last activity on GitHub: 2026-01-05
Why it matters for engineering teams
Kedro addresses the challenge of building data pipelines that are both robust and maintainable in production environments. It provides a structured framework that applies software engineering principles to data science workflows, making it easier for teams to develop reproducible and modular pipelines. This open source tool for engineering teams is particularly suited to machine learning and AI engineering roles focused on operationalising models and managing complex data workflows. Kedro has a strong track record of reliability in production use, supported by a mature codebase and active community. However, it may not be the best choice for projects requiring rapid prototyping or lightweight experimentation, as its structure can introduce overhead in early-stage development or simpler use cases.
When to use this project
Kedro is a production ready solution ideal when teams need to scale data science projects with clear pipeline management and reproducibility. For quick experiments or less structured workflows, teams might consider lighter alternatives that offer more flexibility and faster iteration.
Team fit and typical use cases
Machine learning and AI engineers benefit most from Kedro, using it to build and maintain data pipelines that support model training and deployment. It commonly appears in products requiring reliable data processing workflows and end-to-end machine learning lifecycle management. Teams appreciate its self hosted option for maintaining control over their infrastructure while leveraging a consistent framework for pipeline development.
Best suited for
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2026-01-05. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.