kedro
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.
💡 Why It Matters
Kedro addresses the challenges of building reproducible and maintainable data science pipelines, making it an essential open source tool for engineering teams. It is particularly beneficial for ML/AI teams, including data scientists and machine learning engineers, who require a production-ready solution that adheres to software engineering best practices. With a steady growth in community interest, Kedro demonstrates its maturity and reliability. However, it may not be the right choice for teams looking for a lightweight or quick prototyping tool, as its focus on structure and modularity may introduce complexity in simpler projects.
🎯 When to Use
Kedro is a strong choice when teams need a robust framework for developing scalable data pipelines that require collaboration and long-term maintenance. Teams should consider alternatives if they are working on smaller projects that do not necessitate the overhead of a full pipeline framework.
👥 Team Fit & Use Cases
Kedro is used by data scientists, machine learning engineers, and data engineers who need to implement structured data workflows. It typically fits well in products and systems that involve machine learning model deployment, data processing, and analytics platforms.
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🏷️ Topics & Ecosystem
📊 Activity
Latest commit: 2026-02-13. Over the past 97 days, this repository gained 132 stars (+1.2% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.