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

# Google BigQuery

How to connect using the BigQuery system integration

<div align="left"><figure><img src="https://391958821-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FpokYqd27yrOUxIBhyvbF%2Fuploads%2FA4OP83AowgJrITNARmPd%2FBigQuery-Logo.jpg?alt=media&amp;token=531d7d87-f0ac-43b1-aa1f-50d1b2aaa449" alt=""><figcaption></figcaption></figure></div>

## Overview

Couchdrop can insert data from files directly into Google BigQuery using file actions and Automations.

This allows data received through cloud storage, SFTP/FTP, APIs, or inbound webhooks to be sent into an existing BigQuery table without requiring a separate transfer process.

### How BigQuery works with Couchdrop

BigQuery is configured in Couchdrop as a **System Integration**.

Unlike storage connections, a BigQuery integration does not appear as a Virtual Folder. Instead, the connection is used as a file action inside an Automation.

When configuring the BigQuery file action, you provide information about your BigQuery table schema and define how the data being ingested by Couchdrop maps to that schema.

## How to connect BigQuery

{% hint style="warning" %}

#### Before you start

To connect Google BigQuery to Couchdrop, you will need:

* A Google BigQuery account
* An existing BigQuery table that you want to upload data into
* Your BigQuery **Project ID**
* Your **Dataset name**
* Your **Table name**
  {% endhint %}

{% stepper %}
{% step %}

### Create service account credentials

In Google, navigate to **APIs & Services** > **Credentials**.

Create a service account, then create a key for that account and download the associated JSON credentials file.

You will upload this file when configuring the BigQuery System Integration in Couchdrop.
{% endstep %}

{% step %}

### Create a BigQuery system integration

Log in to Couchdrop and add a new **System Integration**.

Select **BigQuery** from the list of available integrations.
{% endstep %}

{% step %}

### Configure the BigQuery connection

Provide the required BigQuery details and upload your service credentials JSON file.

Click **Add System Integration** to save the connection.
{% endstep %}

{% step %}

### Use BigQuery in an Automation

Create an Automation or file action and select **BigQuery** as the platform type.

Configure the file action with your BigQuery table schema and define how the data being ingested by Couchdrop maps to the table.
{% endstep %}
{% endstepper %}

## BigQuery configuration details

| Configuration                     | Description                                                                                                         |
| --------------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| **Project ID**                    | Identifies the Google Cloud project containing your BigQuery resources.                                             |
| **Dataset name**                  | Identifies the BigQuery dataset containing the destination table.                                                   |
| **Table name**                    | Identifies the existing BigQuery table that will receive the data.                                                  |
| **Service credentials JSON file** | Provides the service account credentials Couchdrop uses to connect to Google BigQuery.                              |
| **Table schema mapping**          | Configured within the file action to define how data being ingested by Couchdrop maps to the BigQuery table schema. |


---

# 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.couchdrop.io/integrations/data-analytics-and-bi/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.