# Google BigQuery

- URL: https://betteranalyst.com/docs/connectors/bigquery
- Updated: 2026-11-12
- Author: Better Analyst

Connect Google BigQuery to Better Analyst using a service account. Analyze large datasets with natural language queries and partitioned table support.

Connect your Google BigQuery data warehouse to analyze large datasets using natural language queries.

## Prerequisites

- A Google Cloud project with BigQuery enabled
- A service account with BigQuery access
- Service account JSON key file

## Setup Steps

### 1. Create a Service Account

1. Go to [Google Cloud Console](https://console.cloud.google.com)
2. Navigate to **IAM & Admin** > **Service Accounts**
3. Click **Create Service Account**
4. Name it (e.g., `formulabot-reader`)
5. Click **Create and Continue**

### 2. Assign Permissions

Grant the service account read access:

1. Add the role: **BigQuery Data Viewer**
2. Add the role: **BigQuery Job User** (required to run queries)
3. Click **Continue** > **Done**

Only grant the minimum required permissions. BigQuery Data Viewer provides read-only access.

### 3. Create a Key

1. Click on the service account you created
2. Go to **Keys** tab
3. Click **Add Key** > **Create new key**
4. Select **JSON** format
5. Download the key file (keep it secure!)

### 4. Connect in Better Analyst

1. Go to **Connectors** > **Add Connector**
2. Select **BigQuery**
3. Upload your service account JSON key
4. Select the project and dataset(s) to access
5. Click **Test Connection**
6. Save when successful

## Project and Dataset Selection

After uploading your service account key:

1. Select the **Project** containing your data
2. Choose which **Datasets** to make available
3. Better Analyst will scan the schemas automatically

You can grant access to multiple datasets from the same project.

## Example Questions

### Basic Queries

- "How many rows are in the events table?"
- "Show me the first 100 rows from users"
- "What columns are in the orders dataset?"

### Aggregations

- "What are total sales by month?"
- "Calculate the average session duration"
- "Count unique users by country"

### Time-Based Analysis

- "Show daily active users for the past 30 days"
- "Compare this week's revenue to last week"
- "What's the hourly traffic pattern?"

### Complex Analysis

- "Join orders with customers and show revenue by segment"
- "Find users who made purchases but never completed onboarding"
- "Calculate cohort retention rates"

## Working with Partitioned Tables

BigQuery tables are often partitioned by date. For best performance:

- Specify date ranges in your questions
- "Show orders from the last 7 days"
- "Analyze data for January 2024"

Including date filters on partitioned tables significantly reduces query costs and improves performance.

## Query Costs

BigQuery charges based on data scanned. Better Analyst:

- Shows estimated query size before running
- Optimizes queries to minimize data scanning
- Respects column pruning (only scans needed columns)

Monitor costs in Google Cloud Console's BigQuery section.

## Troubleshooting

### Permission Denied

- Verify service account has BigQuery Data Viewer role
- Ensure BigQuery Job User role is assigned
- Check the service account has access to the specific dataset

### Invalid Credentials

- Regenerate the service account key
- Ensure you uploaded the full JSON file
- Check the service account hasn't been disabled

### Table Not Found

- Verify the dataset is selected in connector settings
- Check table permissions in BigQuery
- Ensure the table exists in the selected project

### Query Timeout

- Add date filters to reduce data scanned
- Ask for aggregated data instead of raw rows
- Break complex queries into smaller parts

## Best Practices

### Use Specific Date Ranges

Always specify time periods to reduce costs:

- "Last 30 days" instead of "all data"
- "January 2024" instead of "this year"

### Reference Tables Clearly

Include dataset names when asking about specific tables:

- "Show data from analytics.events"
- "Query the sales dataset"

### Limit Large Results

Ask for top N results or aggregations:

- "Top 100 customers by revenue"
- "Total sales by category" (instead of all transactions)

## Next Steps

- [Connect Supabase](https://betteranalyst.com/docs/connectors/supabase)
- [Connect Snowflake](https://betteranalyst.com/docs/connectors/snowflake)
- [Learn about asking questions](https://betteranalyst.com/docs/ai-chat/asking-questions)