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

# BigQuery

> Connect to Google BigQuery data warehouse

## Overview

Connect Plumi to Google BigQuery for serverless, highly-scalable data warehouse analytics.

## Connection Details

| Field           | Description                  |
| --------------- | ---------------------------- |
| Project ID      | Your Google Cloud project ID |
| Dataset         | Default dataset (optional)   |
| Service Account | JSON key file contents       |

## Setting Up 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. Give it a name like `plumi-bigquery-reader`
5. Grant the **BigQuery Data Viewer** role
6. Create a JSON key and download it

### Required Permissions

The service account needs these roles:

* `roles/bigquery.dataViewer` - Read data from tables
* `roles/bigquery.jobUser` - Run queries

```bash theme={null}
# Grant roles via gcloud
gcloud projects add-iam-policy-binding YOUR_PROJECT \
  --member="serviceAccount:plumi@YOUR_PROJECT.iam.gserviceaccount.com" \
  --role="roles/bigquery.dataViewer"

gcloud projects add-iam-policy-binding YOUR_PROJECT \
  --member="serviceAccount:plumi@YOUR_PROJECT.iam.gserviceaccount.com" \
  --role="roles/bigquery.jobUser"
```

## Adding to Plumi

1. Go to **Data Connectors**
2. Select **BigQuery**
3. Enter your Project ID
4. Paste the service account JSON key
5. Test and save

## Query Syntax

BigQuery uses Standard SQL with some extensions:

```sql theme={null}
-- Date functions
SELECT
  DATE_TRUNC(order_date, MONTH) as month,
  SUM(total) as revenue
FROM `project.dataset.orders`
WHERE order_date >= '2024-01-01'
GROUP BY month;

-- Array handling
SELECT
  user_id,
  ARRAY_AGG(product_name) as products
FROM `project.dataset.orders`
GROUP BY user_id;
```

## Cost Optimization

BigQuery charges by data scanned. To minimize costs:

* Select only needed columns (avoid `SELECT *`)
* Use partitioned tables and filter by partition column
* Use `LIMIT` when exploring data
* Consider using clustered tables

## Troubleshooting

### "Access Denied: Project not found"

Verify the project ID is correct and the service account belongs to that project.

### "Access Denied: Dataset not found"

The service account doesn't have access to the dataset. Grant the BigQuery Data Viewer role.