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How to connect?

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 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.

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

bash
1gcloud 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
1gcloud 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 — query BigQuery from AI assistants like Cursor, Claude, and Windsurf.
  • Python Jupyter — explore and analyze your data in Jupyter notebooks.

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