This comprehensive article examines the critical challenges and solutions in real-time data streaming architectures, focusing on two fundamental aspects: temporal accuracy through event-time processing and data integrity through exact-once processing guarantees. It explores how modern streaming frameworks address the inherent challenges of distributed systems, where network delays and component failures can compromise analytical correctness. It investigates watermarking techniques that enable systems to track progress in event time and handle late-arriving data effectively through various windowing strategies. The article then delves into the taxonomy of processing guarantees—at-most-once, at-least-once, and exactly-once—analyzing their respective trade-offs between consistency, availability, and performance. Building blocks for achieving exactly-once semantics are examined in detail, including idempotent operations, transactional event processing patterns, and effective state management through checkpointing. Performance considerations and optimization strategies are evaluated, highlighting how architectural decisions impact latency, throughput, and storage requirements. The integration of temporal and processing guarantees is presented as essential for mission-critical applications, particularly in regulated industries where both timing accuracy and processing integrity directly impact business outcomes.
Keywords: distributed systems reliability, event-time semantics, exactly-once guarantees, stateful fault tolerance, stream processing
