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

# Google BigQuery

<figure><img src="/files/1DXfHtgvRJoTFOR4Krdi" alt=""><figcaption></figcaption></figure>

Google BigQuery integration is similar to GoogleSheets. To start, click “Create New Project” and select Google BigQuery as the primary input. This will take you to the Project Selection page, which will be empty. Select 'Connect Dataset' to begin connecting Google BigQuery.

<figure><img src="/files/CYirlYmlneLhO7ZNZ2R8" alt=""><figcaption></figcaption></figure>

This will trigger a dialog box to sign in with your Google account connected to your BigQuery instance.

<figure><img src="/files/79gBAcGDjuvaPzQ0UYVa" alt=""><figcaption></figcaption></figure>

To use the integration, you'll need to give the required scopes. Read the information carefully before clicking allow.​

<figure><img src="/files/KYHXWHKvlc49zF0iYywe" alt=""><figcaption></figcaption></figure>

Once your account is connected, you can select your project and bring in datasets.

<figure><img src="/files/Sv7vWBnQ5sWfFCE7c3ic" alt=""><figcaption></figcaption></figure>

Due to the structure of Google BigQuery, there are a few layers to getting to what we would consider the dataset. With the project selected, you will select the BigQuery dataset.

<figure><img src="/files/HfX5FyS8Jb6cS9u2JNHv" alt=""><figcaption></figcaption></figure>

This contains a collection of tables (the datasets that the project will be able to interact with).

<figure><img src="/files/uLjHiS9lqDT5djKJkEdr" alt=""><figcaption></figcaption></figure>

After a dataset is connected, the project will look the same as usual; you can merge with another dataset, modify using [Chat Data Prep](https://app.gitbook.com/o/-MUk5bRiktgkKBJI46_9/s/-MUiovvmaDi_ad2DYdZ4/building-a-model/selecting-model-data/chat-data-prep), or make a prediction.


---

# 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 dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.akkio.com/akkio-docs/integrations/google-bigquery.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.