Prerequisites

Google Cloud Project - An active Google Cloud project with BigQuery enabled and dataset access

BigQuery Datasets - Datasets with tables containing your analysis data in BigQuery

IAM Permissions - Service Account file with BigQuery Data Viewer and BigQuery Job User role for read access

Connection Methods

Service Account Key File

Use a service account JSON key file for secure API access.

Connection Guide

Step 1: Access BigQuery Integration

Navigate to Integrations in Datapad and select Google BigQuery:

BigQuery connect screen on Datapad UI

Step 2: Set Up Google Cloud Authentication

Go to Google Cloud Console - IAM & Admin - Service Accounts

Google Cloud service account sidebar

Create a new service account

Google Cloud service account creation

Configure the name, permissions for the Service Account. Grant BigQuery Data Viewer and BigQuery Job roles

Google Cloud service account settings

Generate and download JSON key file

Google Cloud service account setup

Keep your service account key file secure and never share it publicly. This key provides access to your BigQuery data.

Step 3: Configure BigQuery Connection

Enter your Google Cloud project and authentication details:

BigQuery connection form

Required Fields:

Name of the connection
Service Account Key File

Example Queries

Here are some example questions you can ask once your BigQuery data is connected:

"What's our customer acquisition cost trend across all marketing channels this year?"

"How do user engagement metrics vary by geographic region and device type?"

"Which product features drive the highest user retention and revenue?"

"Show me our sales funnel conversion rates by traffic source and campaign?"

"What's the lifetime value distribution across our customer segments?"

"How does seasonal demand affect our inventory and supply chain metrics?"

💬 Big Data Tips

Include dataset and table names for precise targeting in large data warehouses

Use date partitioning references to optimize query performance and costs

Ask about clustering keys and optimization for better query efficiency

Request cost analysis to understand and optimize BigQuery spending

Behind the Scenes

Datapad connects to Google BigQuery using the BigQuery API and generates optimized SQL queries that take advantage of BigQuery's columnar storage, partitioning, and clustering features. Our AI understands BigQuery-specific functions, cost optimization strategies, and performance best practices to provide fast, cost-effective analytics from your big data warehouse.

Troubleshooting

Authentication failed

If BigQuery authentication fails:

Verify your service account key file is valid and not expired
Check that the service account has BigQuery Data Viewer permissions
Ensure the Google Cloud project ID is correct
Try creating a new service account key

Permission denied errors

If you get permission errors:

Verify the service account has access to the specific datasets
Check that BigQuery API is enabled in your Google Cloud project
Ensure IAM roles are properly assigned at the project or dataset level
Contact your Google Cloud admin for additional permissions

Query performance issues

If queries are running slowly or timing out:

Check BigQuery query execution details for optimization opportunities
Verify that tables are properly partitioned and clustered
Consider query optimization or slot reservation for better performance
Monitor BigQuery job history for performance patterns

Cost optimization

If BigQuery costs are higher than expected:

Review query patterns and suggest more efficient alternatives
Check for full table scans that could benefit from partitioning
Consider using clustered tables for better query performance
Set up query cost monitoring and alerts in Google Cloud Console

Google BigQuery Integration