TL;DR¶

This page summarises what you’ve learned about Kedro so far.

Logistics ¶

Kedro can be used on Windows, macOS or Linux
Installation prerequisites include Python 3.7+, git and conda
You should install Kedro using pip install kedro or conda install -c conda-forge kedro

Kedro nodes are the building blocks of pipelines. A node is a wrapper for a Python function that names the inputs and outputs of that function.
A pipeline organises the dependencies and execution order of a collection of nodes.
Kedro has a registry of all data sources the project can use called the Data Catalog. There is inbuilt support for various file types and file systems.
Kedro projects follow a default template that uses specific folders to store datasets, notebooks, configuration and source code.

You can create a Kedro project:
- with just the basic code: kedro new
- or you can populate a new project with pre-built code, e.g. kedro new --starter=pandas-iris from a range of starter projects
Once you’ve created a project, you need to navigate to its project folder; you can then install its dependencies: pip install -r src/requirements.txt
To run the project: kedro run
To visualise the project: kedro viz

Next, you should work through the spaceflights tutorial for the hands-on experience. It illustrates how to build a working project, which includes creating nodes, registering pipelines, setting up the Data Catalog and adding dependencies.
Following the tutorial, you’ll learn how to visualise a Kedro project and learn more about combining Kedro with a Jupyter notebook.