Welcome to Kedro’s documentation!

Introduction

• What is Kedro?
  • Learn how to use Kedro
  • Assumptions

Get Started

• Installation prerequisites
  • Virtual environments
• Install Kedro
  • Verify a successful installation
  • Install a development version
• A “Hello World” example
  • Node
  • Pipeline
  • DataCatalog
  • Runner
  • Hello Kedro!
• Create a new project
  • Create a new project interactively
  • Create a new project from a configuration file
  • Initialise a git repository
  • Create a new project using starters
• Iris dataset example project
  • Background
  • Create the example project
  • Project directory structure
  • What best practice should I follow to avoid leaking confidential data?
  • Run the example project
  • Under the hood: Pipelines and nodes
• Kedro starters
  • How to use Kedro starters
  • List of official starters
  • Starter versioning
  • Use a starter in interactive mode
  • Use a starter with a configuration file

Tutorial

• Kedro spaceflights tutorial
  • Kedro project development workflow
  • Optional: Git workflow
• Set up the spaceflights project
  • Create a new project
  • Install project dependencies
  • kedro install
  • Configure the project
• Set up the data
  • Add your datasets to data
  • Register the datasets
  • Custom data
• Create a pipeline
  • Data engineering pipeline
  • Data science pipeline
  • Kedro runners
  • Partial pipeline runs
• Packaging a project
  • Add documentation to your project
  • Package your project

Kedro Project Setup

• Dependencies
  • Project-specific dependencies
  • kedro install
  • Workflow dependencies
• Configuration
  • Local and base configuration
  • Loading
  • Additional configuration environments
  • Templating configuration
  • Parameters
  • Credentials
  • Configuring kedro run arguments

Data Catalog

• The Data Catalog
  • Using the Data Catalog within Kedro configuration
  • Specifying the location of the dataset
  • Using the Data Catalog with the YAML API
  • Adding parameters
  • Feeding in credentials
  • Loading multiple datasets that have similar configuration
  • Transcoding datasets
  • Transforming datasets
  • Versioning datasets and ML models
  • Using the Data Catalog with the Code API
• Kedro IO
  • Error handling
  • AbstractDataSet
  • Versioning
  • Partitioned dataset

Nodes and Pipelines

• Nodes
  • Creating a pipeline node
  • Tagging nodes
  • Running nodes
  • How to apply decorators to nodes
• Pipelines
  • Building pipelines
  • Developing modular pipelines
  • Connecting existing pipelines
  • Using a modular pipeline twice
  • Using a modular pipeline with different parameters
  • Bad pipelines
  • Running pipelines
  • Running pipelines with IO
  • Outputting to a file
  • Partial pipelines

Extend Kedro

• Custom datasets
  • Scenario
  • Project setup
  • Problem
  • The anatomy of a dataset
  • Implement the _load method with fsspec
  • Implement the _save method with fsspec
  • Implement the _describe method
  • Bringing it all together
  • Integrating with PartitionedDataSet
  • Adding Versioning
  • Thread-safety consideration
  • Handling credentials and different filesystems
  • How to contribute a custom dataset implementation
• Hooks
  • Introduction
  • Concepts
  • Under the hood
• Kedro plugins
  • Overview
  • Initialisation
  • global and project commands
  • Working with click
  • Contributing process
  • Example of a simple plugin
  • Supported plugins
  • Community-developed plugins

Logging

• Logging
  • Configure logging
  • Use logging
  • Logging for anyconfig
• Journal
  • Overview
  • Steps to manually reproduce your code and run the previous pipeline

Development

• Set up Visual Studio Code
  • Advanced: For those using venv / virtualenv
  • Setting up tasks
  • Debugging
  • Configuring the Kedro catalog validation schema
• Set up PyCharm
  • Set up Run configurations
  • Debugging
  • Advanced: Remote SSH interpreter
  • Configuring the Kedro catalog validation schema
• Guide to CLI commands
  • Autocomplete
  • Global Kedro commands
  • Project-specific Kedro commands
  • Using Python
• Linting your Kedro project
• Debugging
  • Introduction
  • Debugging Node
  • Debugging Pipeline

Tools Integration

• Build a Kedro pipeline with PySpark
  • Centralise Spark configuration in conf/base/spark.yml
  • Initialise a SparkSession in ProjectContext
  • Use Kedro’s built-in Spark datasets to load and save raw data
  • Use MemoryDataSet for intermediary DataFrame
  • Use MemoryDataSet with copy_mode="assign" for non-DataFrame Spark objects
  • Tips for maximising concurrency using ThreadRunner
• Use Kedro with IPython and Jupyter Notebooks/Lab
  • Startup script
  • Working with context
  • Adding global variables
  • Working with IPython
  • Working from Jupyter
  • Extras
• How to use Kedro on a Databricks cluster
  • Databricks Connect (recommended)
  • GitHub workflow with Databricks

FAQs

• Frequently asked questions
  • What is Kedro?
  • What are the primary advantages of Kedro?
  • How does Kedro compare to other projects?
  • What is the philosophy behind Kedro?
  • What is data engineering convention?
  • What version of Python does Kedro support?
  • How do I upgrade Kedro?
  • How can I use a development version of Kedro?
  • How can I find out more about Kedro?
  • How can I get my question answered?
• Kedro architecture overview
  • Building blocks

Resources

• Images and icons
  • White background
  • Black background

API Docs

kedro

Kedro is a framework that makes it easy to build robust and scalable data pipelines by providing uniform project templates, data abstraction, configuration and pipeline assembly.

Indices and tables

• Index
• Module Index
• Search Page