> ## Documentation Index
> Fetch the complete documentation index at: https://docs.datazone.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Google BigQuery

> Connect to Google BigQuery for large-scale analytics data extraction.

# Overview

Google BigQuery is a fully managed, serverless data warehouse that enables scalable analysis over large datasets. Datazone connects to BigQuery using a **Google Cloud service account**, allowing you to extract tables and views from any dataset within your GCP project.

## Connection Parameters

| Parameter        | Required | Description                                                                          |
| ---------------- | -------- | ------------------------------------------------------------------------------------ |
| Name             | Yes      | A unique identifier for your BigQuery source                                         |
| Project ID       | Yes      | Your Google Cloud project ID (e.g. `my-gcp-project`)                                 |
| Credentials JSON | Yes      | The full contents of your service account key file in JSON format (stored encrypted) |

## Setting Up a Service Account

1. Go to **IAM & Admin → Service Accounts** in the Google Cloud Console
2. Create a new service account (e.g. `datazone-reader`)
3. Grant the following roles:
   * `BigQuery Data Viewer` — read access to datasets and tables
   * `BigQuery Job User` — permission to run query jobs
4. Create a JSON key for the service account
5. Copy the full contents of the downloaded JSON key file into the **Credentials JSON** field

## Required Permissions

The service account needs the following IAM roles:

* `roles/bigquery.dataViewer` — for reading table data
* `roles/bigquery.jobUser` — for executing queries

For cross-project access, grant `BigQuery Data Viewer` on the specific datasets in the source project.

## Limitations

* BigQuery extracts use the BigQuery Storage API for efficient large-scale reads
* Partitioned and clustered tables are supported
* Supported BigQuery regions: all standard GCP regions

## Next Steps

After configuring your BigQuery source:

1. Create extracts to specify which tables or views to ingest
2. Configure scheduling for recurring extracts
3. Integrate the source into your data pipelines

For more information about working with extracts and pipelines, refer to their respective documentation sections.