Warehouse Performance Benchmark Study (BigQuery)

👉 Read Full Case Study

👉 Download Full report PDF version

In modern cloud warehouses, poorly designed data systems—not slow queries—are the primary source of inefficiency.

Overview

This project benchmarks how different optimization strategies impact query performance and cost in Google BigQuery using large-scale datasets (40M–200M+ rows).

The goal was to understand where performance gains actually come from:

• Writing better SQL
• Designing better data systems

What I Did

I implemented and compared three query strategies:

• Baseline (Naive): No optimization, full table scans
• SQL Optimized: Applied filtering, column reduction, and join improvements
• Architecture Optimized: Partitioned and clustered tables for efficient data access

Each approach was evaluated using:

• Runtime
• Bytes scanned
• Query cost

Follow-up queries were also executed to simulate repeated workloads.

Key Results (from Baseline to Architecture Optimized)

• ~15x faster execution
• ~100x reduction in data scanned
• ~100x reduction in query cost

The largest improvements came from data layout optimization, not just SQL changes.

Why This Matters

BigQuery (and similar cloud warehouses) charge based on data scanned, not compute time.

This means:

• Inefficient queries scale costs quickly
• Optimization directly impacts business spend
• Data architecture decisions have long-term effects

This project demonstrates that:

Designing data systems correctly is more impactful than optimizing queries in isolation.

Tools & Technologies

• Google BigQuery
• SQL
• Public dataset: google_trends
• Query performance metrics (bytes scanned, cost estimation)

How It Was Done

• Built a baseline query joining multiple large tables
• Applied SQL optimizations (filtering early, reducing intermediate data, joins)
• Created partitioned and clustered tables
• Benchmarked performance across strategies
• Compared initial vs repeated query performance

Key Takeaways

• Query optimization provides short-term gains
• Architecture optimization provides scalable, long-term efficiency
• The primary cost driver in BigQuery is data scanned
• Partitioning and clustering dramatically reduce repeated query cost

Project Structure

• queries → SQL scripts for each strategy
• analysis → benchmarking results and comparisons
• visuals → charts and performance screenshots
• report → full case study write-up

Next Steps / Improvements

• Add materialized view benchmarks
• Compare with other warehouses (Snowflake, Redshift)
• Simulate multi-user workloads
• Build a cost monitoring dashboard