---
title: "How to Connect Google BigQuery Database to Preset"
slug: "big-query-database"
description: "Article about how to connect your Google BigQuery database to Preset Cloud."
updated: 2025-11-13T15:09:56Z
published: 2025-11-13T15:09:56Z
canonical: "docs.preset.io/big-query-database"
---

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

# Google BigQuery

## Overview

In this article we will discuss how to connect a Google BigQuery database to Preset. There are two methods that we support to do this:

- [Connect using a Service Account](/v1/docs/big-query-database#connect-using-a-service-account); or
- [Connect via an OAuth2 integration](/v1/docs/big-query-database#connect-using-an-oauth2-integration).

---

## Allowlist Preset IPs

Access to your BigQuery instance might be limited via [VPC Service Controls with IP restrictions](https://cloud.google.com/bigquery/docs/vpc-sc). In this case, you need to allow Preset IPs to interact with BigQuery for a successful connection.

Preset Cloud runs on four regions. Below IPs should be allowed (according to the workspace region):

| us-west-2 (us1a) | us-east-1 (us2a) | eu-north-1 (eu5a) | ap-northeast-1 (ap1a) |
| --- | --- | --- | --- |
| 35.161.45.11 | 44.193.153.196 | 13.48.95.3 | 35.74.159.67 |
| 54.244.23.85 | 52.70.123.52 | 13.51.212.165 | 35.75.171.157 |
| 52.32.136.34 | 54.83.88.93 | 16.170.49.24 | 52.193.196.211 |

If you are not sure where your Preset workspace is located, you can refer to the URL on your browser when accessing Preset. It should look like this: `https://xxxxxxxx.us2a.app.preset.io/superset...`, where `us2a` means it is in `us-east-1`.

---

## Connect using a Service Account

It's possible to use a Service Account to connect BigQuery to Preset. Let's go through this process:

---

### Create & Configure a Google Service Account

What is a Google Service Account?

A service account is a special type of Google account intended to represent a non-human user that needs to authenticate and be authorized to access data in Google APIs.

Access [Google Cloud Platform Console](https://console.cloud.google.com/projectselector2/iam-admin/serviceaccounts?pli=1&supportedpurview=project), and select the Project that contains the BigQuery instance you're looking to connect to Preset. Copy the **Project ID**, as you will need this information to set up the connection in Preset.

You'll be redirected to the **Service Accounts** menu.

1. Click on the **+ CREATE SERVICE ACCOUNT** button. ![Create Service Account.png](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image%28290%29.png)
2. Provide a name for the service account, and optionally a description. ![Service Account Roles.png](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image%28293%29.png)
3. Click on **CREATE AND CONTINUE**.
4. In the **Select a role** dropdown, grant the desired permissions to the service account, according to your needs:
  - If you plan to use Preset for read-only access, granting **BigQuery Data Viewer**, **BigQuery Metadata Viewer**, **BigQuery Read Session User** and **BigQuery Job User** roles should be sufficient.
  - If you also want to execute DML queries in Preset, grant the **BigQuery Data Editor** role as well.

![BQ Roles.png](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image%28292%29.png)

1. Click on **DONE**.

You'll be redirected to the Service Accounts list.

### Create & Save a JSON Key

Our next step is to create and download a private JSON key. This will be used later on when we configure the database connection in Preset.

1. In the list of accounts, click on the service account email.
2. Navigate to the **KEYS** tab.
3. Click on the **ADD KEY** dropdown and select **Create new key**. ![Service Account key creation.png](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image%28294%29.png)
4. The **Create private key** dialog appears. By default, the JSON key type is selected. Click on **CREATE** to download the file to your computer.

Alright! It's time to connect your BigQuery database to Preset.

---

### Setting up the connection In Preset

Let's start by selecting **+ Database** — have a look at [Connecting your Data](/v1/docs/connecting-your-data) if you need help wth this step.

Select **Google BigQuery**.

![BQ Icon.png](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image%28295%29.png)

There are two ways to provide the Service Account credentials:

#### Method One: Using the dynamic form

1. In the **Display Name** field, enter a nickname for your database for display in the Preset environment.
2. Provide the **Project ID** previously copied from Google: ![](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image(313).png)
3. In the **How do you want to enter service account credentials?** field, select:
  1. **Upload JSON file** and then click on **Choose File** to upload the JSON key downloaded earlier; or
  2. **Copy and Paste JSON credentials** and then paste the JSON key content in the **Service Account** text field.
4. *(optional)* Additional connection parameters can be provided in the **Additional Parameters** field.
5. Click on **Connect**.

Great work! You can now make additional configurations to your database, if needed. To learn how to do this, please visit the [Advanced Connection Settings](/v1/docs/advanced-connection-settings) article. When ready, click on **Finish**.

![Complete BQ connection.png](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image%28296%29.png)

---

#### Method Two: Connect via a SQLAlchemy URI

1. Like the previous method, start by selecting the **Google BigQuery** icon, then scroll to the bottom of the Connect a Database panel.
2. Click on **Connect this database with a SQLAlchemy URI string instead**. ![](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image(314).png)
3. The dialog is now organized into two tabs: **Basic** and **Advanced**.
4. In the SQLAlchemy URI field, enter your URI using the following syntax (replace `${Project_ID}` with the actual Project ID):

```plaintext
bigquery://${Project_ID}
```

1. Navigate to the **Advanced** tab and expand the **Security** section.
2. Paste below content in the **Secure Extra** field:

```json
{
  "credentials_info":  ${JSON_KEY}
}
```

1. Open the JSON key downloaded earlier with a text editor, and copy its entire content.
2. Replace `${JSON_KEY}` in the **Secure Extra** field with the copied key. ![BQ Json Key in Advanced.png](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image%28298%29.png)
3. Other configurations can be applied in the **Advanced** tab. If you want to explore them, refer to [Advanced Connection Settings](/v1/docs/advanced-connection-settings).
4. Navigate back to the **Basic** tab and then select **Test Connection**.

Great work! The connection is now created.

## Connect using an OAuth2 integration

It's possible to connect to BigQuery via OAuth2 to apply individual user permissions from BigQuery to the queries that users run in Preset. This approach requires setting up the integration in the Google Cloud Console.

Let's go through this process.

---

### Select the Google Project

Access [Google Cloud Platform Console](https://console.cloud.google.com/projectselector2/iam-admin/serviceaccounts?pli=1&supportedpurview=project), and select the Project that contains the BigQuery instance you're looking to connect to Preset. Copy the **Project ID**, as you will need this information to set up the connection in Preset.

---

### Setting up API & Services

Navigate to the **API & Services** menu. Then follow these steps:

#### Creating an OAuth consent screen

1. Select **OAuth consent screen**, and then **Clients** in the sidebar menu.
2. If you don’t have any client created yet, click on **Get started** to set up the app details:
  1. Provide an **App name** and **User support email** and click on **Next**.
  2. Set the audience to **Internal** and click on **Next**.
  3. Add **Contact information** for the app and click on **Next**.
  4. Agree to the Google API services and click on **Create**.
  5. You’ll be redirected to the **Overview** tab. Navigate back to the **Clients** tab.
3. Click on **+ Create client**.
4. Set **Application type** to **Web application,** and provide a name to the client.
5. Click on **+ Add URI** under the **Authorized redirect URIs** section. Replace `${WORKSPACE_DOMAIN}` in the link below and use it as the URI:

```plaintext
https://${WORKSPACE_DOMAIN}/api/v1/database/oauth2/
```
6. If you plan on using this OAuth connection with multiple Workspaces, repeat step 5 for every Workspace. ![GCP OAuth Client.png](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image%28299%29.png)
7. Click on **Create**.
8. In the confirmation dialog, click on **Download JSON** to retrieve the OAuth credentials. This will be used to set up the OAuth authentication in Preset.

It's time to set up the connection in Preset.

---

### Setting up the connection In Preset

Let's start by selecting **+ Database** — have a look at [Connecting your Data](/v1/docs/connecting-your-data) if you need help wth this step.

Select **Google BigQuery**.

![BQ Icon.png](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image%28295%29.png)

1. In the **Display Name** field, enter a nickname for your database for display in the Preset environment.
2. Change the **Connection method** to **OAuth2** ![](https://cdn.document360.io/4749ddf8-aa05-4f3f-80e1-07a5d2d0f137/Images/Documentation/image(315).png)
3. Provide the **Project ID** previously copied from Google.
4. Open the OAuth2 credentials JSON file downloaded earlier in a text editor to fill the OAuth2 form. Fill the:
  1. **Client ID** field with the `"client_id"` value.
  2. **Client Secret** field with the `"client_secret"` value.
  3. **Authorization Request URI** field with the `"auth_uri"` value.
  4. **Token Request URI** field with the `"token_uri"` value.
  5. **Scope** field with `https://www.googleapis.com/auth/bigquery`.
5. Click on **Connect**.
6. You'll be redirected to the **Advanced** tab.
7. Expand the **Security** section and enable the **Impersonate logged in user** checkbox.
8. Other configurations can be applied in the **Advanced** tab. If you want to explore them, refer to [Advanced Connection Settings](/v1/docs/advanced-connection-settings).
9. Click **Finish** to save your database connection.

To test the integration:

1. Navigate to **SQL** > **SQL Lab**.
2. Select the BigQuery connection in the **Database** dropdown.
3. You should see an **Authorization needed** error. Click on **See more**.
4. Click on the provide authorization hyperlink to be redirected to Google. Authenticate to your Google account and grant consent for the integration.
5. After authentication, you're redirected back to Preset. Refresh the page.

Queries should now be properly authenticated with your OAuth2 token. Note that this **Authorization needed** prompt will be displayed to all users the first time they interact with this database (either in SQL Lab or via charts).