A mid-size fintech company was experiencing frequent data inconsistencies due to unreliable streaming infrastructure. Consumer lag would spike during peak hours, events were occasionally lost, and there was no clear strategy for replaying failed messages.
Approach
1
Audited existing Kafka topology and identified bottlenecks
2
Implemented schema registry with compatibility rules
3
Designed DLQ/quarantine patterns for poison pill handling
4
Built replay tooling with deterministic reprocessing
5
Created SLO dashboards with alerting on consumer lag and event loss
6
Established load testing and chaos engineering practices
Results
99.99% event delivery reliability (up from 99.5%)
Consumer lag spikes reduced by 85%
Mean time to recovery from incidents reduced from 4 hours to 15 minutes
Clear operational runbooks for common failure scenarios
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