5-minute tutorial

Migrate Lightspeed to GCP Dataproc Serverless in 60 Seconds

Learn how to copy your Lightspeed data to GCP Dataproc Serverless 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 Lightspeed and GCP Dataproc Serverless 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
Lightspeed API credentials via OAuth2 app registration
Lightspeed Retail or Restaurant subscription
GCP project
Service account with Dataproc permissions

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 Lightspeed to GCP Dataproc Serverless. This example shows a complete, working command you can adapt to your needs.

Set up your connections

Lightspeed connection format:

lightspeed://?client_id=<your-client-id>&client_secret=<your-client-secret>&refresh_token=<your-refresh-token>

Parameters:

• client_id: OAuth2 client ID from your Lightspeed app
• client_secret: OAuth2 client secret from your Lightspeed app
• refresh_token: OAuth2 refresh token obtained during authorization

GCP Dataproc Serverless connection format:

dataproc-serverless://project_id/region?credentials=/path/to/key.json

Parameters:

Run your first copy

Copy the entire users table from Lightspeed to GCP Dataproc Serverless:

ingestr ingest \
    --source-uri 'lightspeed://?client_id=cid_abc123&client_secret=cs_def456&refresh_token=rt_ghi789' \
    --source-table 'sales' \
    --dest-uri 'dataproc-serverless://project_id/region?credentials=/path/to/key.json' \
    --dest-table 'raw.sales'

What this does:

• Connects to your Lightspeed database
• Reads all data from the specified table
• Creates the table in GCP Dataproc Serverless 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 GCP Dataproc Serverless:

-- Run this in GCP Dataproc Serverless
SELECT COUNT(*) as row_count 
FROM raw.sales;

-- Check a sample of the data
SELECT * 
FROM raw.sales 
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 'lightspeed://?client_id=cid_abc123&client_secret=cs_def456&refresh_token=rt_ghi789' \
    --source-table 'public.orders' \
    --dest-uri 'dataproc-serverless://project_id/region?credentials=/path/to/key.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 Lightspeed to GCP Dataproc Serverless 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 'lightspeed://?client_id=cid_abc123&client_secret=cs_def456&refresh_token=rt_ghi789' \
    --source-table 'public.customers' \
    --dest-uri 'dataproc-serverless://project_id/region?credentials=/path/to/key.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 'lightspeed://?client_id=cid_abc123&client_secret=cs_def456&refresh_token=rt_ghi789' \
    --source-table 'public.transactions' \
    --dest-uri 'dataproc-serverless://project_id/region?credentials=/path/to/key.json' \
    --dest-table 'warehouse.transactions_historical'

Development Environment Sync

Copy production data to your development GCP Dataproc Serverless instance (with sensitive data excluded).

# Copy sample data to development
ingestr ingest \
    --source-uri 'lightspeed://?client_id=cid_abc123&client_secret=cs_def456&refresh_token=rt_ghi789' \
    --source-table 'public.products' \
    --dest-uri 'dataproc-serverless://project_id/region?credentials=/path/to/key.json' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Troubleshooting Guide

Solutions to common issues when migrating from Lightspeed to GCP Dataproc Serverless.

Connection refused or timeout errors

Check your connection details:

Authentication failures

Common authentication issues:

Schema or data type mismatches

Handling data type differences:

ingestr automatically handles most type conversions

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 Lightspeed to GCP Dataproc Serverless with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.

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