5-minute tutorial
Migrate Google Sheets to Google BigQuery in 60 Seconds
Learn how to copy your Google Sheets data to Google BigQuery with a single command using ingestr - no code required.
What you'll learn
Prerequisites
- Python 3.8 or higher installed
- Google account with Sheets API enabled
- Service account created in Google Cloud
- Sheet shared with service account email
- Sheets API enabled in GCP project
- Google Cloud project with BigQuery API enabled
- Service account with BigQuery Data Editor and Job User roles
- Downloaded service account JSON key file
- Dataset created in BigQuery (or permissions to create one)
Step 1: Install ingestr
Install ingestr in seconds using pip. Choose the method that works best for you:
Recommended: Using uv (fastest)
# Install uv first if you haven't already
pip install uv
# Run ingestr using uvx
uvx ingestr
Alternative: Global installation
# Install globally using uv
uv pip install --system ingestr
# Or using standard pip
pip install ingestr
Verify installation: Run ingestr --version
to confirm it's installed correctly.
Step 2: Your First Migration
Let's copy a table from Google Sheets to Google BigQuery. This example shows a complete, working command you can adapt to your needs.
Set up your connections
Google Sheets connection format:
googlesheets://credentials_path@spreadsheet_id/sheet_name
Parameters:
- • credentials_path: Service account JSON file
- • spreadsheet_id: ID from sheet URL
- • sheet_name: Name of specific sheet tab
Google BigQuery connection format:
bigquery://project-id?credentials_path=/path/to/service-account.json
Parameters:
- • project-id: Your Google Cloud project ID
- • credentials_path: Path to service account JSON key file
- • location: Optional dataset location (e.g., US, EU)
BigQuery Setup Required
Before running the command:
- Create a service account in Google Cloud Console
- Grant it BigQuery Data Editor and Job User roles
- Download the JSON key file
- Use the path to this file in your connection string
Run your first copy
Copy the entire users table from Google Sheets to Google BigQuery:
ingestr ingest \
--source-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
--source-table 'Sheet1' \
--dest-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
--dest-table 'raw.Sheet1'
What this does:
- • Connects to your Google Sheets database
- • Reads all data from the specified table
- • Creates the table in Google BigQuery if needed
- • Copies all rows to the destination
Command breakdown:
--source-uri
Your source database--source-table
Table to copy from--dest-uri
Your destination--dest-table
Where to write data
Step 3: Verify your data
After the migration completes, verify your data was copied correctly:
Check row count in Google BigQuery:
-- Run this in BigQuery console
SELECT COUNT(*) as row_count
FROM `raw.Sheet1`;
-- Check a sample of the data
SELECT *
FROM `raw.Sheet1`
LIMIT 10;
Advanced Patterns
Once you've mastered the basics, use these patterns for production workloads.
Only copy new or updated records since the last sync. Perfect for daily updates.
ingestr ingest \
--source-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
--source-table 'public.orders' \
--dest-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
--dest-table 'raw.orders' \
--incremental-strategy merge \
--incremental-key updated_at \
--primary-key order_id
How it works: The merge strategy updates existing rows and inserts new ones based on the primary key. Only rows where updated_at
has changed will be processed.
Common Use Cases
Ready-to-use commands for typical Google Sheets to Google BigQuery scenarios.
Daily Customer Data Sync
Keep your analytics warehouse updated with the latest customer information every night.
# Add this to your cron job or scheduler
ingestr ingest \
--source-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
--source-table 'public.customers' \
--dest-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
--dest-table 'analytics.customers' \
--incremental-strategy merge \
--incremental-key updated_at \
--primary-key customer_id
Historical Data Migration
One-time migration of all historical records to your data warehouse.
# One-time full table copy
ingestr ingest \
--source-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
--source-table 'public.transactions' \
--dest-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
--dest-table 'warehouse.transactions_historical'
Development Environment Sync
Copy production data to your development Google BigQuery instance (with sensitive data excluded).
# Copy sample data to development
ingestr ingest \
--source-uri 'googlesheets:///path/to/creds.json@1a2b3c4d5e/Sheet1' \
--source-table 'public.products' \
--dest-uri 'bigquery://dev-project?credentials_path=/path/to/credentials.json' \
--dest-table 'dev.products' \
--limit 1000 # Only copy 1000 rows for testing
Troubleshooting Guide
Solutions to common issues when migrating from Google Sheets to Google BigQuery.
Connection refused or timeout errors
Check your connection details:
- Share sheet with service account email
- Enable Google Sheets API in GCP
- Verify spreadsheet ID from URL
- Check service account permissions
- Verify the project ID matches your Google Cloud project
- Ensure the service account has necessary permissions
- Check if BigQuery API is enabled in your project
- Confirm the credentials file path is absolute, not relative
Authentication failures
Common authentication issues:
- Share sheet with service account email
- Enable Google Sheets API in GCP
- Verify spreadsheet ID from URL
- Check service account permissions
- Verify the project ID matches your Google Cloud project
- Ensure the service account has necessary permissions
- Check if BigQuery API is enabled in your project
- Confirm the credentials file path is absolute, not relative
Schema or data type mismatches
Handling data type differences:
- ingestr automatically handles most type conversions
- Google Sheets: All data is text by default
- Google Sheets: Date formatting varies by locale
- Google Sheets: Number formatting affects parsing
- Google Sheets: Formula cells vs values
- Google BigQuery: JSON/JSONB types become STRING (use JSON functions to query)
- Google BigQuery: Arrays are converted to JSON arrays
- Google BigQuery: TIMESTAMP types are preserved with timezone information
Performance issues with large tables
Optimize large data transfers:
- Use incremental loading to process data in chunks
- Run migrations during off-peak hours
- Split very large tables by date ranges using interval parameters
Ready to scale your data pipeline?
You've learned how to migrate data from Google Sheets to Google BigQuery with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.