5-minute tutorial

Migrate Google BigQuery to DuckDB in 60 Seconds

Learn how to copy your Google BigQuery data to DuckDB with a single command using ingestr - no code required.

One command Zero code Production ready

What you'll learn

How to install and set up ingestr in seconds

Connect to Google BigQuery and DuckDB with proper authentication

Copy entire tables or specific data with a single command

Set up incremental loading for continuous data synchronization

Prerequisites

Python 3.8 or higher installed
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)
DuckDB installed locally or database file accessible
Write permissions for database file location
Sufficient memory for in-memory operations
Compatible file format version

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 BigQuery to DuckDB. This example shows a complete, working command you can adapt to your needs.

Set up your connections

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)

DuckDB connection format:

duckdb:///path/to/database.duckdb

Parameters:

• path: Path to database file (use :memory: for in-memory)
• read_only: Optional flag for read-only access
• threads: Number of threads to use

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 BigQuery to DuckDB:

ingestr ingest \
    --source-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
    --source-table 'events' \
    --dest-uri 'duckdb:///home/user/analytics.duckdb' \
    --dest-table 'raw.events'

What this does:

• Connects to your Google BigQuery database
• Reads all data from the specified table
• Creates the table in DuckDB 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 DuckDB:

-- Run this in DuckDB
SELECT COUNT(*) as row_count 
FROM raw.events;

-- Check a sample of the data
SELECT * 
FROM raw.events 
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 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
    --source-table 'public.orders' \
    --dest-uri 'duckdb:///home/user/analytics.duckdb' \
    --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 BigQuery to DuckDB 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 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
    --source-table 'public.customers' \
    --dest-uri 'duckdb:///home/user/analytics.duckdb' \
    --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 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
    --source-table 'public.transactions' \
    --dest-uri 'duckdb:///home/user/analytics.duckdb' \
    --dest-table 'warehouse.transactions_historical'

Development Environment Sync

Copy production data to your development DuckDB instance (with sensitive data excluded).

# Copy sample data to development
ingestr ingest \
    --source-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
    --source-table 'public.products' \
    --dest-uri 'duckdb:///home/user/analytics.duckdb' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Troubleshooting Guide

Solutions to common issues when migrating from Google BigQuery to DuckDB.

Connection refused or timeout errors

Check your connection details:

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
Ensure database file path is accessible
Check file permissions for read/write access
Verify DuckDB version compatibility
Consider memory limits for large operations

Authentication failures

Common authentication issues:

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
Ensure database file path is accessible
Check file permissions for read/write access
Verify DuckDB version compatibility
Consider memory limits for large operations

Schema or data type mismatches

Handling data type differences:

ingestr automatically handles most type conversions
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
DuckDB: LIST and STRUCT types for complex data
DuckDB: Native support for nested data structures
DuckDB: Automatic type inference from files
DuckDB: Efficient NULL handling

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 BigQuery to DuckDB with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.

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