Resilience Engineering in Large-Scale Integration Platforms: Lessons from Kafka-Backed Failure Recovery Models
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I1P128Keywords:
Resilience Engineering, Apache Kafka, Distributed Systems, Fault Tolerance, Exactly-Once Semantics, KRaft Protocol, Disaster Recovery, Event-Driven Architecture, Microservices, Cloud-Native SystemsAbstract
Enterprise architectures are shifting—microservices are replacing monoliths—so system uptime now relies less on simple bug fixes, requiring a Resilience Engineering perspective. This paper examines architectural strategies and recovery models in large-scale integration platforms specifically Apache Kafka as the foundation for durable messaging and state management. This research examines the shift from fault tolerance to resilience—robustness adaptability recovery—to pinpoint the mechanisms enabling platform survival and thriving under stress. The study examines Kafka’s replication protocols including the In-Sync Replica (ISR) mechanism and the transition to the KRaft consensus protocol to understand how metadata consistency and leader election impact recovery time objectives. This paper also examines high-level resilience patterns like circuit breakers retry budgets and exactly once semantics (EOS) using industry insights from LinkedIn Uber and Netflix deployments. We derived a resilience framework for mission-critical integration layers from academic benchmarks and industry cases; this framework aims to maintain data integrity and system functionality despite severe regional outages or cascading failures.
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