# kedro-org/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.

Repository: https://github.com/kedro-org/kedro
Canonical: https://ross.abutalabs.com/products/kedro
Homepage: https://kedro.org
Language: Python
License: NOASSERTION
License Family: other
Topics: kedro, hacktoberfest, mlops, python, machine-learning, machine-learning-engineering, agentic-ai, agentic-workflow, data-pipelines
Last push: 2026-08-26T17:45:24+00:00

## Health v2 (maintenance only)
Score: 92/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 78, longevity 100
- inputs: {"age_days": 2694, "days_push": 7, "days_rel": 65, "gap_med": 43.5, "n_releases_24m": 15}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10971, forks 1071 (observed 2026-08-28T04:10:44.910384+00:00)

## What it is
Kedro is an open-source Python framework for building production-ready data engineering and data science pipelines using software engineering best practices. It provides project scaffolding, a Data Catalog for data connectors, and modular pipeline abstractions with nodes and dependency resolution.

## Use cases
- build reproducible data science pipelines in python
- structure a machine learning project for production
- create modular data engineering pipelines
- manage data loading and saving across s3, gcp, and azure
- standardize analytics code across a team
- transition exploratory notebooks into maintainable pipelines
- visualize data pipeline dependencies and lineage

## When to choose
- you need reproducible, maintainable data or ML pipelines in Python
- you want standardized project structure for a data science team
- you need a data catalog abstracting many file formats and cloud storage backends
- you want to move from notebooks to production-ready code

## When to avoid
- you need a full workflow scheduler with cron-style triggers - pair Kedro with Airflow instead
- you only need lightweight ad-hoc scripting without project structure
- your stack is not Python-based

## Facets
- artifact type: framework
- maturity: stable
- function: etl, workflow-automation, data-science, machine-learning, developer-tools
- domain: machine-learning, data-science, developer-tools
- platform: python, windows, cross-platform
- tags: data-pipelines, mlops, data-catalog, pipeline-orchestration, project-template, kedro-viz, data-engineering, automation, linux, macos

## Member repositories
- kedro-org/kedro (main) score 92

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:44.910384+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:17:16.451770+00:00, confidence not recorded.
  - readme: https://github.com/kedro-org/kedro (fetched 2026-08-28T04:10:44.910384+00:00, sha 1aee844df6ef)
  - homepage: https://kedro.org (fetched 2026-08-29T08:16:00.242935+00:00, sha 784b56599635)
  - site_page: https://docs.kedro.org/en/stable (fetched 2026-08-29T08:16:00.252245+00:00, sha d20c60b09c23)
  - site_page: https://docs.kedro.org/en/stable/getting-started/kedro_concepts (fetched 2026-08-29T08:16:00.254156+00:00, sha be11098fc397)
  - site_page: https://docs.kedro.org/en/stable/getting-started/install (fetched 2026-08-29T08:16:00.256116+00:00, sha 88de5bdbd334)
  - registry_pypi: https://pypi.org/pypi/kedro/json (fetched 2026-08-29T08:16:00.257954+00:00, sha 6df409d6cb1c)
- Data as of 2026-08-30T08:39:29.467469+00:00.