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Decision brief
Kedro is ideal for productionizing data science projects through its emphasis on software engineering discipline within the Python environment.
Good fit when
- When your project demands reproducibility and maintainability in pipelines, leveraging Kedro's focus on modular design can greatly enhance these attributes.
- For teams seeking to integrate rigorous version control and dependency management practices into their data science workflow, Kedro provides a structured approach.
Avoid when
- Avoid using Kedro if your project requirements favor minimal setup and quick experimentation over robust pipeline architecture; its structured framework might feel restrictive.
- If the team is not well-versed in Python or software engineering best practices, Kedro may pose a learning curve that could delay project progress.
- Requirements:
- Installable via pip or conda, ideal for Python environments.
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install kedroSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Kedro is designed to help create reproducible, maintainable, and modular data engineering and data science pipelines using software engineering best practices.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 3, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 3, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 3, 2026)
To install Kedro from the Python Package Index (PyPI) run:Source link
Tags
README
How do I install Kedro?
To install Kedro from the Python Package Index (PyPI) run:
uv pip install kedro
It is also possible to install Kedro using conda:
conda install -c conda-forge kedro
Our Get Started guide contains full installation instructions, and includes how to set up Python virtual environments.
Installation from source
To access the latest Kedro version before its official release, install it from the main branch.
uv pip install git+https://github.com/kedro-org/kedro@main
For agents
This page has a .md twin and JSON over the API.