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

# BigQuery

> Connect with BigQuery and define your metrics and dimensions in Push.ai.

## Prerequisites

To connect BigQuery to Push.ai, you need the following:

* The ability to create service accounts in Google Cloud console.

## Setup Guide

### Step 1 - Create a service account in the Google Cloud console

If you're not already familiar with Google Cloud IAM you can follow this [link to create service account.](https://console.cloud.google.com/iam-admin/serviceaccounts/create?pli=1)

### Step 2 - Grant permissions

Select and grant the `BigQuery Data Viewer` and `BigQuery User` roles

### Step 3 - Create a key and download the file

Download the JSON key file and save it to your computer.

### Step 4 - Whitelist IP Addresses (if necessary)

Push.ai connects to your warehouse from the following IP addresses. Be sure to allow traffic from these IPs in your firewall, and include them in any database grants.

| Region/Deployment | IP Addresses                                                                                            |
| ----------------- | ------------------------------------------------------------------------------------------------------- |
| All               | `44.226.145.213`, `54.187.200.255`, `34.213.214.55`, `35.164.95.156`, `44.230.95.183`, `44.229.200.200` |

### Step 5 - Configure the connection in Push.ai

1. Copy the `project_id` from URL
2. Upload the JSON key file
3. Test and create the datasource connection.

## Enhanced Security Controls

To implement dataset level permissioning, please see [GCP's most up-to-date documentation](https://cloud.google.com/bigquery/docs/control-access-to-resources-iam#grant_access_to_a_dataset).