> For the complete documentation index, see [llms.txt](https://docs.mediarithmics.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mediarithmics.io/connectors/sources/google-bigquery.md).

# Google BigQuery

Connect a Google BigQuery project to mediarithmics for data warehouse synchronisation.

Google BigQuery is one of the two supported cloud data warehouse providers, and one of the two connectors that bring data **into** mediarithmics rather than sending it out. Connecting it lets mediarithmics read data held in your BigQuery project, and - when you use a synchronisation strategy that handles updates and deletes - write to a dedicated working schema.

<table data-search="false"><thead><tr><th width="220">Connection</th><th></th></tr></thead><tbody><tr><td><strong>Mechanism</strong></td><td>Data warehouse sync</td></tr><tr><td><strong>Category</strong></td><td><a href="/connectors/partner-types/data-warehouse.md">Data warehouse</a></td></tr><tr><td><strong>Data exchanged</strong></td><td>Whatever your BigQuery datasets expose to the service account</td></tr><tr><td><strong>Authentication</strong></td><td>Dedicated service account, credentials JSON file</td></tr></tbody></table>

{% hint style="info" %}
The data warehouse feature is in Alpha. Contact your account manager to learn more.
{% endhint %}

## Description

### What you can do with this connector

* Make data held in BigQuery available to mediarithmics.
* Use a synchronisation strategy that handles updates and deletes, through a dedicated working schema.

## Connector setup

The full setup is documented in the **Setup & Monitor** section, under *Collect & manage your data › Sync with your data warehouse › Warehouse compatibilities*. It covers, in order:

1. **Enabling the Cloud Resource Manager API** on the BigQuery project.
2. **Creating a working schema**, if your synchronisation strategy needs one.
3. **Creating a dedicated service account** and granting it the right roles - including the limited-access alternative when you would rather not grant `bigquery.dataViewer` at project level.
4. **Generating the credentials JSON file**.

The connection itself, and the choice of synchronisation strategy, are covered on the same page's parent, *Sync with your data warehouse*.

## Partner documentation

Google BigQuery documents its own platform at <https://docs.cloud.google.com/bigquery/docs>. Use it for anything that happens on the partner side of this connector: account setup, the fields the partner expects, and its own limits.

## Related pages

<table data-view="cards" data-search="false"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Snowflake</strong></td><td>The other supported warehouse provider.</td><td><a href="/connectors/sources/snowflake.md">Snowflake</a></td></tr><tr><td><strong>Data warehouse</strong></td><td>What a warehouse connection needs, and why it sits apart from the feeds.</td><td><a href="/connectors/partner-types/data-warehouse.md">Data warehouse</a></td></tr></tbody></table>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.mediarithmics.io/connectors/sources/google-bigquery.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.