Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Financial trading services in the modern world require ultra-low-latency processing, high throughput, and strict adherence to Service Level Agreement (SLA) to enable real-time order processing and settlement. However, as trade increases, distributed microservice architectures pose significant issues for message routing, integration, and system scalability. The report presents a granular architecture for real-time trade processing that leverages Apache Camel and Apache Kafka to process events efficiently in high-workload areas. Apache Kafka is suitable for distributed streaming and can process massive volumes of trade events. Apache Camel can be used to route, transform, and orchestrate data in accordance with enterprise integration models. The proposed architecture employs SLA-conscious optimization policies, including Kafka topic partitioning, asynchronous Camel routing, and adaptive backpressure and latency-monitoring systems, to construct scalable, robust, and high-performance trade processing infrastructures in the current financial setting.