Plugins in Dataiku DSS¶
Dataiku’s integration of code allows you to accomplish anything within the platform through custom code. Plugins allow you to extend the Dataiku GUI by sharing your custom code in various ways.
You can install existing plugins from the store, by uploading a Zip file, or from a Git repository.
In these tutorials, you will learn how to:
- Write your own dev plugins
- Share plugins within your team or publicly through repos
- Developing plugins requires that you have a good working knowledge of Python and/or R.
- You must belong to a group that has the Develop Plugins permission.
With the permission to develop plugins, you can turn on Plugin development by going to Administration > Settings > Misc > Plugin Development
Create a Dev Plugin¶
Before starting any of the tutorials, you’ll need a new dev plugin. You can create a new plugin for each tutorial, or use the same plugin for all of them. To create a dev plugin:
- From the application menu, choose Plugins Development.
- Click +New dev plugin.
- Give an identifier to your plugin, like
This identifier should be globally unique. Prefixing plugin id’s with your company name, or something similar, will help prevent conflicting names if you import plugins from 3rd party sources.
Now that we have a skeleton for a plugin, we can add some components to it.
A plugin is made of a number of components. Each component is a single kind of object in Dataiku DSS, such as a dataset, recipe, or webapp.
These tutorials walk through development of different types of components.
Congratulations! You’ve taken your first steps to learning about Dataiku plugins.
- See the partitioned custom datasets how-to for another written explanation of a plugin.
- Our reference guide on plugins contains more information on all the available components you can include in a plugin.
- The plugins gallery contains descriptions of the Dataiku plugins that can be installed through the Plugins “store” in Dataiku DSS.
- The code of the publicly available Dataiku plugins is managed in a GitHub repository. In particular, have a look at the samples folder which lists which feature each dataset and recipe in the public repository use.