Reference | Navigation bar#

The Navigation bar is key to moving between different areas of Dataiku, such as the Flow, machine learning models, notebooks, and dashboards.

You can find the Navigation bar at the top of the screen in a project. It includes several menu icons, starting with the Flow icon.

In many of these menu options, you’ll find a list of elements in your project and options to create new objects. Read on to learn the options in each menu!


The options available in your menus may vary depending on the version and features used on your Dataiku instance.

Flow menu#

The Flow menu in the Navigation bar.

The first menu, the Flow menu, is used to return to the Flow from other screens within the project, or to see lists of datasets and recipes used in the Flow. Options include:




The Flow (visual overview) of the project. From the Flow, you can then navigate to any recipe, dataset, model, or other component of the project.


List of datasets in the project.


List of recipes in the project.

Labeling tasks

List of labeling tasks in the project. Labeling tasks are created and managed through the Labeling visual recipe.

Streaming Endpoints

List of endpoints that handle continuously streaming data.

Visual Analyses menu#

The Visual Analyses menu in the Navigation bar.

The Visual Analyses menu is where you’ll find machine learning models and other tools used to evaluate and compare them. Options in this menu include:



Visual Analyses

List of machine learning models or interactive data preparation created in the Lab.

Saved Models

List of models that have been deployed from the Lab to the Flow.

Prompt Studios

Visual interface that allows you to work with large language model (LLM) prompts.

Model Evaluation Stores

Space to collect evaluations of different models that can be used for drift analyses.

Model Comparisons

Space to visually compare the performance of models from multiple evaluations.

Experiment Tracking

Space to store and compare code-based machine learning models. For more information, see the reference documentation on experiment tracking.

Notebooks menu#

The Notebooks menu in the Navigation bar.

The Notebooks menu takes you to code-related spaces in Dataiku. Options include:




List of all your notebooks, which are interactive coding environments to interact with or manipulate data using a coding language such as Python, R, or SQL.


List of your webapps, which are custom applications hosted by Dataiku and used for advanced visualizations or custom frontends.

R Markdown Reports

List of generated documents from a mixture of Markdown and code. For more information, see the Concept | R Markdown reports article.


The Library Editor, where you can create your own libraries or helpers and share then within the project for use in Python or R recipes and notebooks.

Code Studios

List of Code Studios, where you can edit code elements in Dataiku with your favorite integrated development environment (IDE), such as JupyterLab or RStudio.

Jobs menu#

The Jobs menu in the Navigation bar.

The Jobs menu helps you monitor automation in Dataiku. Options include:




List of jobs, or activities happening in the Flow, such as running recipes or executing code.


List of scenarios, or automated tasks such as building datasets or training models.

Automation Monitoring

Space to monitor scenario runs via a daily summary or timeline.

Wiki menu#

The Wiki menu in the Navigation bar.

The Wiki menu takes you to a space where you can create and edit documentation for your project.




List of articles to document your project.

Dashboards menu#

The dashboards menu in the Navigation bar.

The Dashboards menu navigates to visual reporting tools that help you collaborate and communicate insights about your project.




List of dashboards, which are tools to publish elements such as tables, charts, and model reports.


List of elements that you can add to dashboards.

More options#

The More options menu in the Navigation bar.

The More options menu (the three dots to the right of the Dashboards menu) navigates to areas to deploy your models and control other project-level settings.



API Designer

API services, which allow you to deploy machine learning models for real-time scoring.


Space to create global or local variables, which can be used in visual or code recipes, scenarios,and applications.


List of bundles, or versioned snapshots of your project.


Space to create one-shot actions to maintain your project.

Version control

List of commits on your project. For more information, see the reference documentation on version control.

Application Designer

Options to convert your project into a visual application or an application-as-recipe.


Controls for project-level settings, such as project tags, integrations, automation, engines, and connections.


Project-level security options, including permissions, API keys, and shared objects.