5-minute tutorial

Copy Data from Snowflake to DuckDB with ingestr

Learn how to move Snowflake data into DuckDB with a repeatable CLI workflow using ingestr.

One command Zero code Production ready

What you'll learn

How to install and set up ingestr in seconds
Connect to Snowflake 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
  • Snowflake account with active warehouse
  • User credentials with appropriate permissions
  • Database and schema access rights
  • Network policies allowing connections from your IP
  • 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 Snowflake to DuckDB. This example shows a complete, working command you can adapt to your needs.

Set up your connections

Snowflake connection format:

snowflake://user:password@account/database/schema?warehouse=warehouse_name

Parameters:

  • • user: Snowflake username
  • • password: User password
  • • account: Account identifier (including region)
  • • database: Target database name
  • • schema: Schema within the database
  • • warehouse: Compute warehouse to use
  • • role: Optional role to assume

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

Run your first copy

Copy the entire users table from Snowflake to DuckDB:

ingestr ingest \
    --source-uri 'snowflake://myuser:[email protected]/mydb/public?warehouse=compute_wh' \
    --source-table 'raw_events' \
    --dest-uri 'duckdb:///home/user/analytics.duckdb' \
    --dest-table 'raw.raw_events'

What this does:

  • • Connects to your Snowflake 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.raw_events;

-- Check a sample of the data
SELECT * 
FROM raw.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 'snowflake://myuser:[email protected]/mydb/public?warehouse=compute_wh' \
    --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 Snowflake 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 'snowflake://myuser:[email protected]/mydb/public?warehouse=compute_wh' \
    --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 'snowflake://myuser:[email protected]/mydb/public?warehouse=compute_wh' \
    --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 'snowflake://myuser:[email protected]/mydb/public?warehouse=compute_wh' \
    --source-table 'public.products' \
    --dest-uri 'duckdb:///home/user/analytics.duckdb' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Choosing a Snowflake to DuckDB data integration tool

If you're comparing ways to move Snowflake data into DuckDB, 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 Snowflake to DuckDB?

ingestr is a good fit when you want an open-source CLI for Snowflake to DuckDB 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 Snowflake to DuckDB 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 Snowflake to DuckDB.

Connection refused or timeout errors

Check your connection details:

  • Verify account identifier includes region (e.g., xy12345.us-east-1)
  • Check if warehouse is running and not suspended
  • Ensure user has USAGE privilege on warehouse
  • Confirm network policies allow your IP address
  • 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 account identifier includes region (e.g., xy12345.us-east-1)
  • Check if warehouse is running and not suspended
  • Ensure user has USAGE privilege on warehouse
  • Confirm network policies allow your IP address
  • 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
  • Snowflake: VARIANT type for semi-structured data
  • Snowflake: ARRAY and OBJECT types for complex structures
  • Snowflake: Automatic timezone conversion for TIMESTAMP_TZ
  • 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 Snowflake to DuckDB with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.

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