Event Sourcing with Kafka Streams: A Comprehensive Benchmark Against CRUD for Financial Workloads

Modern financial applications demand architectures that ensure high throughput, low latency, and strong resilience. Traditional CRUD-based systems often encounter transactional bottlenecks and limited fault tolerance, making them less suitable for real-time workloads. In this work, we design and benchmark a Kafka-based event-sourcing architecture that integrates CQRS, schema-registry-backed versioning, Zstandard compression, dynamic Dead Letter Queue (DLQ) handling, and RocksDB-backed local state caching.We evaluate this system against a CRUD-based REST+SQL implementation. To ensure a rigorous comparison, we enforce network symmetry by deploying both the Kafka cluster and the PostgreSQL database on managed cloud infrastructure, eliminating placement bias. Benchmarks were conducted on a dataset of 50,000 banking events (generating over 150,000 persistent state transitions) to measure sustained performance. Our findings show that the Kafka-based approach achieves a sustained throughput of 105.4 events/sec (2.69x over CRUD) while maintaining consistent low-latency reads, validating its suitability for production-grade financial platforms.