# Databox integrations: data sources and datasets

> Create a data source, create a dataset and create a metric in Databox. Connect Databox to the rest of your data and analytics stack with Tray.ai.

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

## Overview

Databox is a business analytics tool that pulls metrics from many sources into dashboards, called Databoards, for tracking marketing, sales, and company performance. Tray.ai connects to Databox through the HTTP Connector, so your workflows can call the Databox API alongside the rest of your stack.

## Use cases

### Data sources

Read, create, update, and delete data sources in Databox from any Tray.ai workflow. The Databox API covers calls such as list data sources and create a data source.

- List data sources
- Create a data source
- Update a data source

### Datasets

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

- List datasets
- Create a dataset
- Ingest records

### Metrics

Read, create, update, and delete metrics in Databox from any Tray.ai workflow. The Databox API covers calls such as list metrics and create a metric.

- List metrics
- Create a metric
- Update a metric

### Databoards

Read databoards in Databox from any Tray.ai workflow. The Databox API covers calls such as list Databoards and get Databoard metrics.

- List Databoards
- Get Databoard metrics

### Users

Read, create, update, and delete users in Databox from any Tray.ai workflow. The Databox API covers calls such as list users and create a user.

- List users
- Create a user
- Update a user

### Accounts

Read, create, update, and delete accounts in Databox from any Tray.ai workflow. The Databox API covers calls such as list accounts and create an account.

- List accounts
- Create an account
- Update an account

## Challenges Tray.ai solves

### Keeping Databox 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 Databox 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 Databox 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 Databox 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 Databox?

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

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

The Databox API uses an API key in a request header. You save it once as a Tray.ai authentication, and the HTTP Connector adds it to every request.

### Can I get a dedicated Databox connector?

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