Kedro architecture overview

There are different ways to leverage Kedro in your work, you can:

  • Commit to using all of Kedro (framework, project, starters and library); which is preferable to take advantage of the full value proposition of Kedro

  • You can use parts of Kedro, like the DataCatalog (I/O), OmegaConfigLoader, Pipelines and Runner, by using it as a Python library; this best supports a workflow where you don’t want to adopt the Kedro project template

  • Or, you can develop extensions for Kedro e.g. custom starters, plugins, Hooks and more

At a high level, Kedro consists of five main parts:

Kedro architecture diagram

Kedro project

As a data pipeline developer, you will interact with a Kedro project, which consists of:

  • The conf/ directory, which contains configuration for the project, such as data catalog configuration, parameters, etc.

  • The src directory, which contains the source code for the project, including:

    • The pipelines directory, which contains the source code for your pipelines.

    • settings.py file contains the settings for the project, such as library component registration, custom hooks registration, etc. All the available settings are listed and explained in the project settings chapter.

    • pipeline_registry.py file defines the project pipelines, i.e. pipelines that can be run using kedro run --pipeline.

    • __main__.py file serves as the main entry point of the project in package mode.

  • pyproject.toml identifies the project root by providing project metadata, including:

    • package_name: A valid Python package name for your project package.

    • project_name: A human readable name for your project.

    • kedro_init_version: Kedro version with which the project was generated.

Kedro framework

Kedro framework serves as the interface between a Kedro project and Kedro library components. The major building blocks of the Kedro framework include:

  • session is responsible for managing the lifecycle of a Kedro run.

  • context holds the configuration and Kedro’s main functionality, and also serves as the main entry point for interactions with core library components.

  • hooks defines all hook specifications available to extend Kedro.

  • cli defines built-in Kedro CLI commands and utilities to load custom CLI commands from plugins.

Kedro starter

You can use a Kedro starter to generate a Kedro project that contains boilerplate code. We maintain a set of official starters but you can also use a custom starter of your choice.

Kedro library

Kedro library consists of independent units, each responsible for one aspect of computation in a data pipeline:

  • OmegaConfigLoader provides utility to parse and load configuration defined in a Kedro project.

  • pipeline provides a collection of abstractions to model data pipelines.

  • runner provides an abstraction for different execution strategy of a data pipeline.

  • io provides a collection of abstractions to handle I/O in a project, including DataCatalog and many Dataset implementations.

Kedro extension

You can also extend Kedro behaviour in your project using a Kedro extension, which can be a custom starter, a Python library with extra hooks implementations, extra CLI commands such as Kedro-Viz or a custom library component implementation.

If you create a Kedro extension, we welcome all kinds of contributions. Check out our guide to contributing to Kedro. Dataset contributions to kedro-datasets are the most frequently accepted, since they do not require any changes to the framework itself. However, we do not discourage contributions to any of the other kedro-plugins.