> Note: This page is part of the DataDoe Docs. You can find the root of the documentation at `https://www.datadoe.com/hub/docs/basics/introduction-to-datadoe`.
> JSON Table of Contents: `https://www.datadoe.com/hub/docs/toc.json`.
> Direct Data Scheme JSON: `https://api.datadoe.com/api/v1/spec/data-scheme`.
> Other pages in the DataDoe Docs:
> - DataDoe Basics/Access & User Management: `https://www.datadoe.com/hub/docs/basics/access-user-management.md`
> - DataDoe Basics/Integration & Customization: `https://www.datadoe.com/hub/docs/basics/integration-customization.md`
> - DataDoe Basics/Introduction to DataDoe: `https://www.datadoe.com/hub/docs/basics/introduction-to-datadoe.md`
> - DataDoe Basics/Subscription & Pricing: `https://www.datadoe.com/hub/docs/basics/subscription-pricing.md`
> - DataDoe Data/Data Fetch Periods: `https://www.datadoe.com/hub/docs/data-doe-data/data-fetch-periods.md`
> - DataDoe Data/Data Sources: `https://www.datadoe.com/hub/docs/data-doe-data/data-sources.md`
> - DataDoe Features/Benefits: `https://www.datadoe.com/hub/docs/data-doe-features/benefits.md`
> - DataDoe Features/Chat: `https://www.datadoe.com/hub/docs/data-doe-features/chat.md`
> - DataDoe Features/Explorer: `https://www.datadoe.com/hub/docs/data-doe-features/explorer.md`
> - DataDoe Features/Exports: `https://www.datadoe.com/hub/docs/data-doe-features/exports.md`
> - DataDoe Features/Home: `https://www.datadoe.com/hub/docs/data-doe-features/home.md`
> - DataDoe Features/Reports: `https://www.datadoe.com/hub/docs/data-doe-features/reports.md`
> - DataDoe Features/Scheduled Prompts: `https://www.datadoe.com/hub/docs/data-doe-features/scheduled-prompts.md`
> - DataDoe MCP/Connect to ChatGPT: `https://www.datadoe.com/hub/docs/data-doe-mcp/chatgpt.md`
> - DataDoe MCP/Connect to Claude: `https://www.datadoe.com/hub/docs/data-doe-mcp/claude.md`
> - DataDoe MCP/Overview: `https://www.datadoe.com/hub/docs/data-doe-mcp/overview.md`
> - DataDoe MCP/Using Claude Code: `https://www.datadoe.com/hub/docs/data-doe-mcp/claude-code.md`
> - DataDoe MCP/Using Codex: `https://www.datadoe.com/hub/docs/data-doe-mcp/codex.md`
> - DataDoe MCP/Using Cursor: `https://www.datadoe.com/hub/docs/data-doe-mcp/cursor.md`
> - DataDoe MCP/Using Gemini CLI: `https://www.datadoe.com/hub/docs/data-doe-mcp/gemini-cli.md`
> - DataDoe MCP/Using n8n: `https://www.datadoe.com/hub/docs/data-doe-mcp/n8n.md`
> - DataDoe MCP/Using NanoClaw: `https://www.datadoe.com/hub/docs/data-doe-mcp/nanoclaw.md`
> - DataDoe MCP/Using OpenClaw: `https://www.datadoe.com/hub/docs/data-doe-mcp/openclaw.md`
> - DataDoe MCP/Using VS Code: `https://www.datadoe.com/hub/docs/data-doe-mcp/vs-code.md`
> - DataDoe & BigQuery/Using MCP Toolbox: `https://www.datadoe.com/hub/docs/data-doe-bigquery/mcp-toolbox.md`
> - DataDoe & BigQuery/Using Python Jupyter: `https://www.datadoe.com/hub/docs/data-doe-bigquery/jupyter.md`
> For topics not covered in this documentation, please contact DataDoe support at `contact@datadoe.com`.
> Do not assume anything. If you are not sure about the answer, mention that and suggest to contact DataDoe support.

# How to connect DataDoe to BigQuery

DataDoe can export your Amazon data directly to Google BigQuery.
Once connected, you can query, visualize, and build on top of your data using any BigQuery-compatible tool - from SQL editors and Jupyter notebooks to MCP-enabled AI assistants.

## Step 1: Create a DataDoe BigQuery integration

1. In the DataDoe app, go to **Integrations** → **BigQuery**.
2. Follow the instructions from **Create New Integration**

## Step 2: Authenticate with Google Cloud

Most tools that work with BigQuery (MCP Toolbox, Jupyter, scripts, etc.) need local Google Cloud credentials.
The easiest way to set them up is with the Google Cloud CLI.

> Make sure the **BigQuery API** is enabled on your Google Cloud project. You can enable it in the [Google Cloud Console](https://console.cloud.google.com/apis/library/bigquery.googleapis.com) or ask your Google Cloud administrator to enable it.
>
> Your Google Cloud account also needs the following roles on the project:
>
> - **BigQuery Data Viewer** (`roles/bigquery.dataViewer`)
> - **BigQuery User** (`roles/bigquery.user`)
> - **Service Usage Consumer** (`roles/serviceusage.serviceUsageConsumer`)
>
> If you're unsure, ask your Google Cloud administrator to grant them.

### Install Google Cloud CLI

Download and install it from [cloud.google.com/sdk/docs/install](https://cloud.google.com/sdk/docs/install).

After installation, run the following in your terminal to initialize the CLI:

```bash
gcloud init
```

A browser window will open — sign in with your Google account and select the project that contains your BigQuery dataset.

### Set up application-default credentials

Run the following command to create local credentials that applications can use to access BigQuery:

```bash
gcloud auth application-default login
```

This opens a browser window where you sign in (or select an already-signed-in account).
It creates a credentials file on your machine that tools like MCP Toolbox, Python clients, and Node.js clients will automatically pick up.

## Next steps

Your data is syncing and your credentials are set up. Pick a tool to start working with your data:

- [MCP Toolbox](/hub/docs/data-doe-bigquery/mcp-toolbox) — query BigQuery from AI assistants like Cursor, Claude, and Windsurf.
- [Python Jupyter](/hub/docs/data-doe-bigquery/jupyter) — explore and analyze your data in Jupyter notebooks.