Project Title

kedro — A Python toolbox for production-ready, reproducible, and modular data science pipelines

Overview

Kedro is an open-source Python framework designed to facilitate the creation of data engineering and data science pipelines that are reproducible, maintainable, and modular. It adheres to software engineering best practices, making it ideal for production environments. Kedro stands out for its project template, which provides a standardized starting point for data science projects, and its integration with various data sources and machine learning frameworks.

Key Features

Project Template: A standard, modifiable, and easy-to-use project template based on best practices.
Data Engineering: Supports building robust data engineering pipelines.
Modularity: Encourages the development of modular code for scalability and maintainability.

Use Cases

Data Scientists: Use Kedro to create and manage complex data science projects with ease.
Production Environments: Deploy data pipelines that are reproducible and maintainable in production settings.
Educational Purposes: Teach software engineering best practices in data science through a standardized project structure.

Advantages

Best Practices: Encourages the use of software engineering best practices in data science projects.
Community Support: Active community and support through Slack, ensuring quick resolution of issues.
Extensibility: Easily extendable to accommodate various data sources and machine learning frameworks.

Limitations / Considerations

Learning Curve: May require an initial investment in learning the framework for those new to Kedro.
Project Customization: While the project template is a strength, heavy customization might require significant changes to the template structure.

Dagster: A data orchestrator for building data applications. It differs from Kedro in its focus on data orchestration rather than project structure.
Prefect: A workflow management system that focuses on automation and monitoring. It differs in its approach to workflow definition and execution.
MLflow: An open-source platform for managing the end-to-end machine learning lifecycle. It differs in its focus on machine learning lifecycle management rather than project structure.

Basic Information

GitHub: https://github.com/kedro-org/kedro
Stars: 10,540
License: Apache 2.0
Last Commit: 2025-09-21

📊 Project Information

Project Name: kedro
GitHub URL: https://github.com/kedro-org/kedro
Programming Language: Python
⭐ Stars: 10,540
🍴 Forks: 963
📅 Created: 2019-04-18
🔄 Last Updated: 2025-09-21

🏷️ Project Topics

Topics: [, ", e, x, p, e, r, i, m, e, n, t, -, t, r, a, c, k, i, n, g, ", ,, , ", h, a, c, k, t, o, b, e, r, f, e, s, t, ", ,, , ", k, e, d, r, o, ", ,, , ", m, a, c, h, i, n, e, -, l, e, a, r, n, i, n, g, ", ,, , ", m, a, c, h, i, n, e, -, l, e, a, r, n, i, n, g, -, e, n, g, i, n, e, e, r, i, n, g, ", ,, , ", m, l, o, p, s, ", ,, , ", p, i, p, e, l, i, n, e, ", ,, , ", p, y, t, h, o, n, ", ]