Kedro-Viz is a blueprint of your data and machine-learning workflows. It provides data lineage, keeps track of machine-learning experiments, and makes it easier to collaborate with business stakeholders.
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.
Amazon SageMaker, Apache Airflow, Apache Spark, Azure ML, Dask, Databricks, Docker, fsspec, Jupyter Notebook, Kubeflow, Matplotlib, MLflow, Plotly, Pandas, VertexAI, and more.
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.
The extension integrates Kedro projects with Visual Studio Code, providing features like enhanced code navigation and autocompletion for seamless development.
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.
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 science code.
Eduardo Ohe, Principal Data Engineer
Kedro is an open-source project. Go ahead and install it with pip or conda:
pip install kedro
or
conda install -c conda-forge kedro
For more details, see the set up documentation or watch the video.