Maximizing Value, Minimizing Spend – Snowflake Cost Optimization

Solution:

Phase 1: Cost Visibility & Usage Insights

• Custom Dashboards: Built on ACCOUNT_USAGE to track compute/storage use by user, team, project.
• Workload Analysis: Mined QUERY_HISTORY and WAREHOUSE_METERING_HISTORY for inefficiencies, peak loads, and idle time.

Phase 2: Compute Efficiency Optimization

• Warehouse Rightsizing: Resized oversized warehouses (e.g., L → M/S) based on actual usage.
• Auto-Suspend/Resume: Set idle timeouts (2–5 mins) to cut down compute waste.
• Multi-Cluster Scaling: Tuned for high-concurrency scenarios like faculty reports and data loads.
• Query Optimization Workshops: Trained users on clustering keys, materialized views, indexing.
• Resource Monitors: Set credit limits and alerts at warehouse/department levels to control spend.
• Query Tags for Usage Attribution: Leveraged query tags to categorize and analyze compute usage across ETL processes, reporting workloads, and individual users—enabling granular visibility into cost drivers.

Phase 3: Storage Optimization & Governance

• Data Retention Policies: Automated archival/purging of stale data to trim storage.
• Zero-Copy Cloning: Enabled test environments without duplicating data.
• Compression & Table Tuning: Refined structures and leveraged Snowflake compression.

Cost Showback Model: Collaborated with finance and department heads to implement a cost showback model, attributing compute and storage usage to business units. We also developed a report that identifies key drivers behind monthly compute cost increases, helping teams take proactive cost-control actions.