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Data Analytics / SaaS

Accelerating Analytics Workloads with DuckDB

A data analytics startup reduced query times from 45 seconds to 200ms using DuckDB, eliminating expensive data warehouses.

Executive Summary

A SaaS analytics startup built on PostgreSQL was too slow—dashboards took 45 seconds to load. Migrating to DuckDB with Parquet storage reduced query times to 200ms while cutting infrastructure costs by 80%.

Key Outcomes

  • ▹ 45s → 200ms query time (99.6% reduction)
  • ▹ Infrastructure cost reduced 80%
  • ▹ Analysts can now query 100GB datasets locally

Client Situation

Customer dashboards timed out frequently, causing support tickets and churn. The PostgreSQL database couldn't handle growing data volume.

Key Challenges

  • ⚠ Dashboards timing out after 30 seconds
  • ⚠ PostgreSQL slow on analytical queries
  • ⚠ $50k/month Snowflake bill too expensive

Existing Architecture

PostgreSQL for OLTP and OLAP (mixed workload). Aggregation queries scanning millions of rows.

  • No columnar storage for analytics
  • Indexes insufficient for ad-hoc queries
  • Compute and storage coupled (expensive scaling)

Solution Design

DuckDB as analytical engine with Parquet files on S3, replacing PostgreSQL for reporting.

Key Decisions

  • ✓ DuckDB for in-process analytics (no separate server)
  • ✓ Parquet columnar storage (10x compression)
  • ✓ S3 as data lake for all raw events
DuckDBParquetS3PythonStreamlit

Implementation

Parallel run for 1 month, comparing DuckDB queries against PostgreSQL before cutover.

  1. Phase 1: Phase 1: Data Export

    Converted 5TB of PostgreSQL data to Parquet on S3.

  2. Phase 2: Phase 2: Query Migration

    Rewrote 50 dashboard queries for DuckDB SQL dialect.

  3. Phase 3: Phase 3: Production Rollout

    Switched dashboard backend to DuckDB with zero downtime.

Technical Challenges

DuckDB memory limit on large aggregations

Impact: Queries failing on 64GB RAM limit

Resolution: Partitioned aggregations + external hash join using temp files

SQL dialect differences

Impact: PostgreSQL-specific functions not supported

Resolution: Implemented custom DuckDB extension for missing functions

Results

Dashboard P95 query time
Before45 seconds
After200ms
Improvement99.6% reduction
Monthly infrastructure cost
Before$50,000
After$10,000
Improvement80% reduction
Data volume queried
Before10GB
After100GB
Improvement10x increase

Lessons Learned

  • 📘 Parquet compression reduced storage 10x (5TB → 500GB)
  • 📘 DuckDB's vectorized execution 100x faster than PostgreSQL for analytics
  • 📘 Analysts loved being able to query locally with DuckDB CLI

What We Would Do Differently

  • 💡 Use DuckDB's native HTTPfs to query S3 directly
  • 💡 Implement incremental Parquet updates earlier

Role Relevance

DuckDB engineers understood columnar storage, vectorized execution, and Parquet optimization—transforming slow dashboards into interactive experiences.

Critical Skills Demonstrated

DuckDB internalsParquet optimizationAnalytical query tuningData lake architecture

Related Roles

DuckDB Engineer Data Engineer Analytics Engineer

Frequently Asked Questions

Why DuckDB over other OLAP databases?
In-process execution (no separate server) and 100x faster than PostgreSQL on analytics.
How do you handle concurrent users?
Read-only replicas with DuckDB for each dashboard pod—no lock contention.