What percentage of queries couldn't migrate?

20% required Snowflake for massive joins or extremely large aggregations beyond 64GB RAM.

How did you handle the migration risk?

Parallel execution for 1 month, comparing DuckDB vs Snowflake results before cutover.

How does this case study work?

Raise a request, talk to experts, fund the project, expert works, review and approve payment. All remote, all through our platform.

Reducing Data Warehouse Costs Using DuckDB

Executive Summary

A marketing analytics firm's Snowflake bill reached $200k/year as data volume grew. Migrating to DuckDB on EC2 with Parquet storage reduced costs by 90% while improving query performance for 80% of workloads.

Key Outcomes

▹ $200k → $20k annual cost (90% reduction)
▹ Query performance improved 3x for aggregations
▹ No performance degradation for 80% of queries

Client Situation

The firm processed 50TB of marketing data monthly. Snowflake costs grew linearly with data volume, threatening profitability.

Key Challenges

⚠ $16k/month Snowflake bill increasing 20% quarterly
⚠ Hard to predict costs—surprise bills common
⚠ Most queries were recurring dashboards, not ad-hoc

Existing Architecture

Snowflake as primary data warehouse. dbt for transformations. Tableau for dashboards.

Compute cost high even for recurring queries
No ability to optimize for specific workload patterns
Cold storage still expensive for historical data

Solution Design

DuckDB on EC2 spot instances with Parquet on S3, keeping Snowflake for ad-hoc analysis only.

Key Decisions

✓ DuckDB for 80% of queries (recurring dashboards)
✓ S3 Parquet as data lake (5x cheaper than Snowflake storage)
✓ dbt running on EC2 instead of Snowflake

DuckDBAWS EC2ParquetdbtAirflow

Implementation

Migrated non-critical dashboards first, then critical ones after validation.

Phase 1: Phase 1: POC
Proved DuckDB could run 80% of queries faster at 10% cost.
Phase 2: Phase 2: dbt Migration
Moved dbt transformations from Snowflake to DuckDB on EC2.
Phase 3: Phase 3: Dashboard Cutover
Switched 200 Tableau dashboards to DuckDB backend.

Technical Challenges

dbt on DuckDB missing features

Impact: Incremental models and macros not fully compatible

Resolution: Forked dbt-duckdb with custom patches for missing features

Concurrent query handling

Impact: Single DuckDB process bottlenecks with 10+ concurrent dashboards

Resolution: Read-only replicas per dashboard pod (horizontal scaling)

Results

Annual data warehouse cost: Before$200,000
After$20,000
Improvement90% reduction
Query performance (80th percentile): Before2.5 seconds
After0.8 seconds
Improvement3x faster
Cost predictability: BeforeVariable
AfterFixed EC2 pricing
Improvement100% predictable

Lessons Learned

📘 80/20 rule applied: 80% of queries were recurring and perfect for DuckDB
📘 EC2 spot instances (70% discount) further reduced costs
📘 Keeping Snowflake for 20% of ad-hoc queries provided flexibility

What We Would Do Differently

💡 Use DuckDB's HTTPfs for direct S3 queries earlier
💡 Implement query result caching for dashboard reuse

Role Relevance

DuckDB engineers designed the hybrid architecture that cut costs by 90% while maintaining performance, saving $180k annually.

Critical Skills Demonstrated

Cost optimizationHybrid cloud architecturedbt on DuckDBS3/Parquet optimization

Frequently Asked Questions

What percentage of queries couldn't migrate?: 20% required Snowflake for massive joins or extremely large aggregations beyond 64GB RAM.
How did you handle the migration risk?: Parallel execution for 1 month, comparing DuckDB vs Snowflake results before cutover.