Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Modern distributed streaming systems face significant challenges in maintaining data integrity when processing high-volume, real-time data streams. Network partitions, node failures, and retry mechanisms can introduce data duplication or loss, compromising system reliability. This article presents a comprehensive framework for implementing exactly-once processing guarantees in large-scale streaming architectures. The framework encompasses transactional event processing models, idempotent operation design principles, and sophisticated checkpointing mechanisms that ensure data consistency across distributed nodes. Key components include fault-tolerant state recovery protocols, incremental snapshot techniques, and coordination mechanisms that maintain processing guarantees under various failure scenarios. The article compares exactly-once semantics with at-least-once and at-most-once alternatives, identifying optimal use cases for each guarantee type. Performance implications of exactly-once processing are evaluated, including latency overhead, throughput considerations, and resource utilization patterns. Optimization strategies such as incremental checkpointing and efficient state management are presented to mitigate performance costs. The framework addresses practical implementation challenges in existing streaming platforms while providing guidance for architects designing robust, large-scale data processing systems that prioritize correctness without compromising operational efficiency.