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

# BigQuery

{% embed url="<https://youtu.be/3SvQtLHcyxA?si=OBX_mqWLK4RFmc70>" %}

Connect to [Google BigQuery](https://cloud.google.com/bigquery) to analyze and visualize your cloud warehouse data directly in Felt.

{% hint style="success" %}
This feature is only available to customers on the [Enterprise plan](https://felt.com/pricing). To upgrade, [contact sales](https://felt.com/sales).
{% endhint %}

1. Create a new, read-only user on your database for Felt access
2. Open **New data source** — from the workspace homepage, click **+** next to **Data sources**. Or from a map, click ![](/files/qv8POfyKJ00VfTgugMR2) in the toolbar and choose **+ New data source**.
3. Select **BigQuery**
4. Enter Connection Details
   1. **Source Name**: name of the source in Felt
   2. **Project ID**: the unique identifier for a Google Cloud Platform project
   3. **Dataset**: (*optional*) name of the BigQuery dataset. Datasets are “top-level containers used to organize and control access to your tables”. [Learn more](https://cloud.google.com/bigquery/docs/datasets-intro#datasets).
   4. **Service account JSON**: Upload a service account JSON with permissions to access your BigQuery tables. To create a new service account, navigate to the [**IAM & Admin**](https://console.cloud.google.com/iam-admin/serviceaccounts) page of the Google Cloud Console and generate a JSON file for it in the **Keys** tab. When creating the account, make sure to grant it access to your project for the **BigQuery Data Viewer**, **BigQuery Metadata Viewer** and **BigQuery Job User** roles.
5. Click **Connect**
6. Once connected you will see a catalog of your data with previews for your new source
7. From here you can add any of these layers to your maps!

{% hint style="warning" %}
If your BigQuery contains [EXTERNAL tables](https://cloud.google.com/bigquery/docs/external-tables) (such as Google Sheets or objects in Cloud Storage), you must also make sure the service account has the following [permissions](https://cloud.google.com/bigquery/docs/access-control):

* `storage.objects.get`
* `storage.objects.list`
  {% endhint %}

{% hint style="info" %}
Custom queries and views in BigQuery have an output limit of 10GB. For queries or views larger than this, switch to using tables (BigQuery tables do not have this limit) or filter the data further to lower the output size.
{% endhint %}


---

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