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Fondant makes it easy to build data preparation pipelines leveraging reusable components. Fondant provides a lot of components out of the box (overview), but you can also define your own custom components.

The anatomy of a component#

A component is completely defined by its component specification and a docker image. The specification defines the docker image fondant should run to execute the component, which data it consumes and produces, and which arguments it takes.

Component types#

We can distinguish two different types of components:

  • Reusable components can be used out of the box and can be loaded from the fondant component registry
  • Custom components are completely defined and implemented by the user

Reusable components#

Reusable components are out of the box components from the Fondant hub that you can easily add to your pipeline:

dataset = dataset.apply(
    "arg": "value"

You can find an overview of the available reusable components on the Fondant hub. Check their documentation for information on which arguments they accept and which data they consume and produce.

Custom components#

To define your own custom component, you can build your code into a docker image and write an accompanying component specification that refers to it.

A typical file structure for a custom component looks like this:

|- components
|  |- custom_component
|     |- src
|     |  |-
|     |- Dockerfile
|     |- fondant_component.yaml
|     |- requirements.txt

The Dockerfile is used to build the code into a docker image, which is then referred to in the fondant_component.yaml.

name: Custom component
description: This is a custom component
image: custom_component:latest

You can add a custom component to your pipeline by passing in the path to the directory containing your fondant_component.yaml.
dataset = dataset.apply(
    "arg": "value"

See our best practices on creating a custom component.