5-minute tutorial

Migrate Google BigQuery to Databricks in 60 Seconds

Learn how to copy your Google BigQuery data to Databricks 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 Databricks 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)
Databricks workspace with SQL endpoint
Personal access token generated
SQL endpoint running (not terminated)
Appropriate permissions on catalog/schema

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 Databricks. 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)

Databricks connection format:

databricks://token@host:port/http_path

Parameters:

• token: Personal access token (use as username)
• host: Workspace URL
• port: Port number (usually 443)
• http_path: SQL endpoint HTTP path

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 Databricks:

ingestr ingest \
    --source-uri 'bigquery://my-project?credentials_path=/path/to/credentials.json' \
    --source-table 'events' \
    --dest-uri 'databricks://[email protected]:443/sql/1.0/endpoints/abc123' \
    --dest-table 'raw.events'

What this does:

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

-- Run this in Databricks
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 'databricks://[email protected]:443/sql/1.0/endpoints/abc123' \
    --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 Databricks 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 'databricks://[email protected]:443/sql/1.0/endpoints/abc123' \
    --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 'databricks://[email protected]:443/sql/1.0/endpoints/abc123' \
    --dest-table 'warehouse.transactions_historical'

Development Environment Sync

Copy production data to your development Databricks 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 'databricks://[email protected]:443/sql/1.0/endpoints/abc123' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Troubleshooting Guide

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

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 SQL endpoint is running
Verify personal access token is valid
Check workspace URL is correct
Confirm HTTP path matches your endpoint

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 SQL endpoint is running
Verify personal access token is valid
Check workspace URL is correct
Confirm HTTP path matches your endpoint

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
Databricks: Delta tables support schema evolution
Databricks: Complex types (arrays, maps, structs) supported
Databricks: Photon acceleration for certain operations
Databricks: Partitioning affects query performance

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

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