Concept | Dataiku applications#

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Dataiku applications allow you to package and reuse projects for collaboration. Additionally, they let you apply an existing project’s workflow to new data without needing to understand the details of the project.

Creating a Dataiku application#

A typical process for creating and using a Dataiku application is:

  • A data scientist creates a Dataiku project.

  • They realize the workflows in the project could be useful to colleagues.

  • The data scientist (or app developer) converts their project into a Dataiku application.

  • Each colleague using the application creates their own instance of the Dataiku application.

Accessing applications#

Dataiku users can find applications on the homepage in a dedicated Applications section or access them from the Applications menu.

To manage access, the application designer can configure the execute permissions for the application.

Visual applications and applications-as-recipes#

Dataiku applications can be surfaced in two different ways:

  • Visual applications allow you to package a project with a GUI on top, which empowers more people within an organization to leverage AI and self-service analytics.

  • Applications-as-recipes allow you to package part of a Flow into a recipe usable in the Flows of other projects. These recipes can be executed externally via the public REST API.

Use case for a Dataiku application#

Let’s say a data-driven marketing team relies on a single data scientist to build dashboards for monitoring performance by region and quarter. The data scientist receives multiple requests each day for up-to-date data using different input parameters.

Instead of manually resolving each request, the data scientist can package her project into an easy-to-use Dataiku application, so the members of the marketing team can choose the parameters they want and quickly retrieve insights for their business needs.

Because each application is its own instance, multiple users can run instances of the same Dataiku application at the same time using different data or parameters.

Dataiku applications vs webapps#

Their names and use cases might be similar, but Dataiku applications and webapps are different and have different purposes in Dataiku.

Webapps#

Webapps are great for developers who want to create custom or interactive visualizations. A webapp can be written in many different programming languages:

  • Standard (written in HTML/CSS/JS, and optionally with a Python backend)

  • R (using the Shiny library)

  • Python (using the Bokeh or Dash libraries)

Dataiku screenshot of a webapp.

Developers create webapps within a Dataiku project. Any user with the correct permissions can access the webapp. Developers can also share their webapps with non-Dataiku users.

While Dataiku does not automatically allow concurrent usage of a webapp, the developer can implement code to allow more than one user to access the same webapp at the same time.

Applications#

Dataiku applications are ideally suited for business teams who need to create and share business applications with many users across an organization.

Dataiku screenshot of a Dataiku application.

For example, this sales forecasting application started out as a project. The designer of the project packaged it to allow users to build their own sales history dashboards by entering information into a custom field. Therefore, users of this Dataiku application can complete specific tasks without having to run the recipes in the project itself.

Coding skills are not required to build applications.

What’s next?#

To better understand Dataiku applications, make sure you are confident creating automation scenarios.

For an interactive learning experience, try the Academy tutorial on Dataiku applications or the course on Visualization, which includes webapps.