This project presents a comprehensive analysis of database performance issues and strategic solutions to improve query execution times for data consumers across an orga nisation.

Project Overview Link to heading

Challenge: Data consumers company-wide were experiencing significantly slow query response times, impacting productivity and user experience.

Objective: Investigate the root causes of performance degradation and propose actionable solutions to improve end-user experience while considering cost implications.

Approach: Systematic analysis of Snowflake query history data to identify bottlenecks and optimisation opportunities.

Case Study Brief Link to heading

The investigation focused on analysing two key datasets containing:

  • Query execution logs with performance metrics
  • Database object access patterns
  • Warehouse utilisation statistics

Key Data Points Analysed Link to heading

Query Performance Metrics:

  • TOTAL_TIME - Complete query execution duration
  • EXECUTION_TIME - Actual processing time
  • QUEUEING_TIME - Time spent waiting in warehouse queue
  • BYTES_SPILLED_TO_REMOTE - Memory overflow indicators

Infrastructure Context:

  • WAREHOUSE_SIZE - Compute resource allocation
  • WAREHOUSE_NAME - Resource distribution patterns
  • QUERY_TYPE - SQL operation categorisation
  • HUMAN_USER - User vs. automated query distinction

Data Access Patterns:

  • DATABASE_ID and SCHEMA_NAME - Resource hotspots
  • SCHEMA_INDEX - Multi-schema query complexity

Methodology Link to heading

The analysis employed a structured approach to problem decomposition:

  1. Performance Baseline Establishment - Quantified current query performance across different dimensions
  2. Bottleneck Identification - Isolated primary causes of slowdowns using statistical analysis
  3. Resource Utilisation Assessment - Evaluated warehouse sizing and queue management
  4. Cost-Benefit Analysis - Balanced performance improvements against operational costs
  5. Solution Prioritisation - Ranked recommendations by impact and implementation complexity

Key Findings & Recommendations Link to heading

The investigation revealed several critical performance bottlenecks and proposed targeted solutions addressing:

  • Warehouse sizing optimisation strategies
  • Query queue management improvements
  • Resource allocation rebalancing
  • Schema access pattern optimisation
  • Memory management enhancements

Detailed analysis, findings, and implementation roadmap are available in the complete presentation below.

Project Deliverable Link to heading

The comprehensive analysis and strategic recommendations are documented in the following presentation:

📊 Download Strategy to Fix Slow Queries SlideDeck

Technical Considerations Link to heading

Data Processing: Given the large dataset size, the analysis utilized tools capable of handling substantial data volumes efficiently.

Audience: Recommendations were tailored for the data guild audience, comprising engineers and analysts responsible for implementation, with focus on:

  • User experience impact
  • Cost optimisation
  • Implementation feasibility
  • Measurable performance improvements

Implementation Impact Link to heading

The proposed solutions provide a framework for:

  • Reducing average query response times
  • Optimising warehouse resource utilisation
  • Improving overall data platform reliability
  • Establishing performance monitoring best practices

This case study demonstrates systematic problem-solving in data performance optimisation, showcasing analytical thinking and practical solution design for enterprise-scale database challenges.