Snowflake Cost Optimization & Performance Improvement

This case study highlights how I optimized Snowflake workloads for an enterprise data platform by addressing warehouse inefficiencies, task queuing, and long-running stored procedures. The initiative resulted in a 30% reduction in warehouse costs while significantly improving execution reliability and performance.

The platform consisted of multiple enterprise databases, each supporting scheduled data processing tasks responsible for deduplication and core business transformations. These tasks were critical for downstream analytics and reporting and were executed at regular intervals throughout the day.

Several performance and cost-related issues were impacting the platform:

• Five Snowflake databases, each running ~40 scheduled tasks.
• All tasks shared a single X-Small warehouse.
• Tasks executed stored procedures containing complex business logic, including:
  • Record deduplication.
  • Data validation and transformation.
• Long execution times caused warehouse queuing.
• Queuing led to.
  • Increased total warehouse runtime.
  • Higher compute costs.
  • Delays in data availability.

The warehouse was frequently active for extended periods, driving up costs without delivering proportional performance.

I identified that the primary cost drivers were not the number of tasks, but

• Under-sized warehouse capacity leading to execution bottlenecks.
• Inefficient stored procedure logic causing prolonged execution times.
• Lack of optimized warehouse configuration for suspend/resume behavior.

The queuing behavior meant the warehouse stayed active longer than necessary, increasing overall consumption.

I implemented a combination of capacity tuning, query optimization, and warehouse configuration improvements:

1. Warehouse Optimization

• Increased warehouse size from X-Small to Small.
• Enabled auto-suspend and auto-resume to prevent idle compute usage.
• Reduced task queuing by allowing faster parallel execution.

2. Stored Procedure & Query Optimization

• Refactored stored procedure logic to improve execution efficiency.
• Optimized SQL patterns used for deduplication and transformations.
• Reduced unnecessary scans and intermediate processing steps.

3. Execution Flow Improvements

• Improved task execution stability.
• Eliminated backlog caused by long-running procedures.
• Ensured predictable completion times for scheduled workloads.

The optimization delivered clear and measurable business value.

• 30% reduction in Snowflake warehouse costs.
• Significant reduction in task execution time.
• Elimination of warehouse queuing during peak schedules.
• Improved reliability of scheduled data pipelines.
• Faster availability of analytics-ready data for business users.

These improvements ensured that the platform scaled efficiently without increasing operational spend.