Streaming systems have become a foundational component of modern cloud-native architectures, enabling real-time data processing and event-driven responsiveness at scale. While these systems excel at handling high-throughput data streams, they often fall short in transforming raw data into meaningful, context-aware intelligence. This limitation stems from an architectural bias toward event-centric processing, where individual events are treated as sufficient triggers for decision-making. This paper introduces a novel perspective on streaming system design by shifting the focus from event processing to context accumulation. It proposes the concept of a Context-Continuity Streaming Architecture, in which events are interpreted as signals that incrementally update an evolving contextual state rather than as isolated decision triggers. This approach enables systems to derive intelligence over time, incorporating temporal patterns, behavioral signals, and contextual dependencies. The study develops a conceptual and architectural framework for building streaming systems that prioritize continuous understanding over immediate reaction. It demonstrates how context-aware models improve resilience to late and out-of-order events, enhance decision quality, and align streaming architectures more closely with real-world intelligence processes. By reframing streaming systems as context-driven rather than event-driven, this work contributes to the evolution of real-time data architectures toward scalable, intelligent, and adaptive systems.
Ilker Kanatli (Fri,) studied this question.