Data integration tool
Best way to move Lightspeed data to MotherDuck
Use ingestr when you need an open-source CLI for Lightspeed to MotherDuck ingestion, then add Bruin Cloud when the same pipeline needs schedules, checks, lineage, alerts, and audit trails.
Short answer
Choose the tool you can run locally and govern later.
For Lightspeed to MotherDuck, ingestr is the practical starting point when you want a scriptable, reviewable ingestion job instead of a hosted-only connector. Use Bruin Cloud when that job becomes a shared production pipeline.
Start with a local CLI command and commit the workflow to your repo.
Use incremental or time-based loading when the source supports it.
Verify row counts and schema expectations before scheduling.
Add Bruin Cloud for orchestration, lineage, checks, alerts, and audit logs.
What you'll learn
Prerequisites
- Python 3.8 or higher installed
- Lightspeed API credentials via OAuth2 app registration
- Lightspeed Retail or Restaurant subscription
- MotherDuck account
- Authentication token
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 ingestrAlternative: Global installation
# Install globally using uv
uv pip install --system ingestr
# Or using standard pip
pip install ingestrVerify installation: Run ingestr --version to confirm it's installed correctly.
Step 2: Your First Migration
Let's copy a table from Lightspeed to MotherDuck. 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
MotherDuck connection format:
motherduck:///?token=<token>Parameters:
- • token: MotherDuck authentication token
Run your first copy
Copy the entire users table from Lightspeed to MotherDuck:
ingestr ingest \
--source-uri 'lightspeed://?client_id=cid_abc123&client_secret=cs_def456&refresh_token=rt_ghi789' \
--source-table 'sales' \
--dest-uri 'motherduck:///?token=your_token_here' \
--dest-table 'raw.sales'What this does:
- • Connects to your Lightspeed database
- • Reads all data from the specified table
- • Creates the table in MotherDuck if needed
- • Copies all rows to the destination
Command breakdown:
--source-uriYour source database--source-tableTable to copy from--dest-uriYour destination--dest-tableWhere to write data
Step 3: Verify your data
After the migration completes, verify your data was copied correctly:
Check row count in MotherDuck:
-- Run this in MotherDuck
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 'motherduck:///?token=your_token_here' \
--dest-table 'raw.orders' \
--incremental-strategy merge \
--incremental-key updated_at \
--primary-key order_idHow 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 MotherDuck 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 'motherduck:///?token=your_token_here' \
--dest-table 'analytics.customers' \
--incremental-strategy merge \
--incremental-key updated_at \
--primary-key customer_idHistorical 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 'motherduck:///?token=your_token_here' \
--dest-table 'warehouse.transactions_historical'Development Environment Sync
Copy production data to your development MotherDuck 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 'motherduck:///?token=your_token_here' \
--dest-table 'dev.products' \
--limit 1000 # Only copy 1000 rows for testingChoosing a Lightspeed to MotherDuck data integration tool
If you're comparing ways to move Lightspeed data into MotherDuck, start with the path you can run locally, review in code, and schedule later.
What is the best data integration tool to move data from Lightspeed to MotherDuck?
ingestr is a good fit when you want an open-source CLI for Lightspeed to MotherDuck ingestion. You can run it from your terminal, CI, or a scheduled job, then move the same pipeline into Bruin Cloud when you need orchestration, lineage, and monitoring.
Can this run as an incremental pipeline?
Yes. Use snapshot-plus-incremental or time-based extraction when the source supports it. That keeps the first load simple while making later runs smaller and easier to monitor.
When should I use Bruin Cloud with ingestr?
Use Bruin Cloud when the Lightspeed to MotherDuck pipeline needs schedules, alerts, data quality checks, audit trails, or catalog and lineage visibility for the rest of the team.
Troubleshooting Guide
Solutions to common issues when migrating from Lightspeed to MotherDuck.
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 MotherDuck with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.