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HTTP/2 200 age: 865422 cache-control: public,max-age=0,must-revalidate cache-status: "Netlify Edge"; hit content-encoding: gzip content-type: text/html; charset=utf-8 date: Mon, 29 Dec 2025 20:56:05 GMT etag: "16hc3bkkjtg1hov" netlify-vary: query=__nextDataReq|_rsc,header=x-nextjs-data|x-next-debug-logging|next-router-prefetch|next-router-segment-prefetch|next-router-state-tree|next-url|rsc|accept-encoding,cookie=__prerender_bypass|__next_preview_data server: Netlify strict-transport-security: max-age=31536000 vary: Accept-Encoding x-content-type-options: nosniff x-nf-request-id: 01KDNYB3N8YPCPMVZMSZWX6HCS x-powered-by: Next.js content-length: 11079 Kedro | An open-source framework for data engineering and data science code

kedro

Why Kedro?Features FAQs Get Started Documentation Blog

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Reproducible
data pipelines solved

Kedro is a toolbox for production-ready data pipelines.

Why Kedro?

Clean Code

Kedro is the foundation for clean data engineering and data science code. It borrows concepts from software engineering and applies them to building data pipelines.

Handles Complexity

A Kedro project provides scaffolding for complex data and machine-learning pipelines. You spend less time on tedious "plumbing" and focus instead on solving new problems.

Standardisation

Kedro standardises how data engineering and data science code is created and ensures teams collaborate to solve problems easily.

Production-Ready

Make a seamless transition from development to production with exploratory code that you can transition to reproducible, maintainable, and modular experiments.

Kedro's Key Concepts Explained →

Features

Pipeline Visualisation

Kedro-Viz is a blueprint of your data and machine-learning workflows. It provides data lineage, surfaces detailed pipeline execution information such as execution time, node status, dataset statistics, and makes it easier to collaborate with business stakeholders.

–– 01

Data Catalog

A series of lightweight data connectors used to save and load data across many different file formats and file systems. The Data Catalog supports S3, GCP, Azure, sFTP, DBFS, and local filesystems. Supported file formats include Pandas, Spark, Dask, NetworkX, Pickle, Plotly, Matplotlib, and many more. The Data Catalog also includes data and model snapshots for file-based systems.

–– 02

Integrations

Amazon SageMaker, Apache Airflow, Apache Spark, Azure ML, Dask, Databricks, Docker, fsspec, Jupyter Notebook, Kubeflow, Matplotlib, MLflow, Plotly, Pandas, VertexAI, and more.

–– 03

Project Template

You can standardise how configuration, source code, tests, documentation, and notebooks are organised with an adaptable, easy-to-use project template. Create your cookie cutter project templates with Starters.

–– 04

Dedicated IDE support

The extension integrates Kedro projects with Visual Studio Code, providing features like enhanced code navigation and autocompletion for seamless development.

–– 05

Pipeline Abstraction

You never have to label the running order of tasks in your pipeline because Kedro supports a dataset-driven workflow that supports automatic resolution of dependencies between pure Python functions.

–– 06

Coding Standards

Test-driven development using pytest, produce well-documented code using Sphinx, create linted code with ruff and make use of the standard Python logging library.

–– 07

Flexible Deployment

Deployment strategies that include single or distributed-machine deployment as well as additional support for deploying on Argo, Prefect, Kubeflow, AWS Batch, AWS Sagemaker, Databricks, Dask and more.

FAQs

You can find the Kedro community on Slack.

We also maintain a list of extensions, plugins, articles, podcasts, talks, and Kedro showcase projects in the awesome-kedro repository.

Expand all

What is Kedro?

Kedro is an open-source Python framework hosted by the Linux Foundation (LF AI & Data). Kedro uses software engineering best practices to help you build production-ready data engineering and data science code.

What does Kedro do?

Is Kedro an orchestrator?

I'm a Data Scientist. Why should I use Kedro?

I'm a Machine-Learning Engineer/Data Engineer. Why should I be interested in Kedro?

I'm a Product Lead, and my team wants to use Kedro. Why?

What's Kedro's origin story?

How can I find out more about Kedro?

Our community

Case studies

Kedro in production at

Learn how Kedro is used in production at Telkomsel, Indonesia's largest telecommunications company. Kedro is used to help consume tens of TBs of data, run hundreds of feature engineering tasks, and serve dozens of ML models.

Kedro in production at

Data scientists at Beamery, a fast-growing talent lifecycle management company, explain how Kedro helps them write "production-code". They talk about a workflow that involves Kedro when they want to progress their POCs.

Testimonials

Eduardo Ohe, Principal Data Engineer

Tremendously valuable

"Kedro has streamlined our workflow process, avoiding a lot of back and forth with debugging. It allowed our company to deliver more value to our customers quickly."

Ghifari Dwiki Ramadhan, Data Engineering

We heavily use Kedro

"We use Kedro in our production environment which consumes tens of TBs of data, runs hundreds of feature engineering tasks, and serves dozens of ML models."