# Dataiku integrations: projects and datasets

> Create a project, create a dataset and create a scenario in Dataiku. Connect Dataiku to the rest of your data and analytics stack with Tray.ai.

**Canonical page:** https://tray.ai/connectors/dataiku-integrations/
**Categories:** Data and analytics
**Connects through:** HTTP Connector

## Overview

Dataiku is a data science and AI platform where teams prepare data, build machine learning models and run automated data pipelines in shared projects. Tray.ai connects to Dataiku through the HTTP Connector, so your workflows can call the Dataiku API alongside the rest of your stack.

## Use cases

### Projects

Read, create, and delete projects in Dataiku from any Tray.ai workflow. The Dataiku API covers calls such as list projects and create a project.

- List projects
- Create a project
- Delete a project

### Datasets

Read, create, update, and delete datasets in Dataiku from any Tray.ai workflow. The Dataiku API covers calls such as create a dataset and get a dataset.

- Create a dataset
- Get a dataset
- Update a dataset

### Scenarios

Read and create scenarios in Dataiku from any Tray.ai workflow. The Dataiku API covers calls such as list scenarios and create a scenario.

- List scenarios
- Create a scenario
- Run a scenario

### Jobs

Read and create jobs in Dataiku from any Tray.ai workflow. The Dataiku API covers calls such as start a job and get a job.

- Start a job
- Get a job
- Abort a job

## Challenges Tray.ai solves

### Keeping Dataiku credentials out of logs

A raw API call usually means pasting a key or token into the request, where it ends up in step inputs, logs, and screenshots.

**How Tray.ai helps:** Store Dataiku credentials once as a Tray.ai authentication and reference them from the HTTP Connector. Tray.ai masks authentication values in logs, so the secret never shows up in a step's input or output.

### Going from the Dataiku API docs to a working call

Translating an example request from API docs into a workflow step means copying the method, URL, headers, and body by hand.

**How Tray.ai helps:** Paste a cURL example from the Dataiku API docs into the HTTP Connector and Tray.ai fills in the method, URL, headers, and body for you.

### Handling files and non-JSON responses

Not every API answers in JSON. Some return XML, files, or binary data, or expect a multipart upload.

**How Tray.ai helps:** The HTTP Connector parses JSON and XML responses, returns files you can pass to the next step, and sends multipart form data for uploads.

## Frequently asked questions

### Does Tray.ai connect to Dataiku?

Yes. Tray.ai connects to Dataiku through the HTTP Connector, which calls the Dataiku API directly. Any call the Dataiku API documents can run as a step in a Tray.ai workflow.

### How does Tray.ai authenticate with Dataiku?

The Dataiku API uses a bearer token and basic authentication. You save it once as a Tray.ai authentication, and the HTTP Connector adds it to every request.

### Can I get a dedicated Dataiku connector?

Dataiku runs through the HTTP Connector rather than a dedicated connector. If your team wants one, the Tray.ai connector SDK can build a dedicated Dataiku connector from the Dataiku API.

## Learn more

- Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/
- Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/
- Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/
- Book a demo: https://tray.ai/contact/