Streaming Intelligence Designing Real-Time Big Data Pipelines for Autonomous Decision Systems

Abstract

Real-time big data pipelines have become a cornerstone of autonomous decision systems in the manufacturing industry, mobility, cyber-physical infrastructure, digital platforms, and safety-critical monitoring. The key issue is how to convert continuous, high-velocity, and heterogeneous event streams into timely, dependable, and operationally relevant decisions in dynamic environments. This review examines the design logic of streaming intelligence by concentrating on architectural patterns, stream-processing models, state-management, event-time reasoning, edge cloud partitioning, and adaptation schemes that are found in peer-reviewed literature. Particular attention is given to the trade-offs among latency, throughput, correctness, fault tolerance, interpretability and decision quality. According to the reports, the current pipelines are becoming more and more event-centric, stateful, elastic, and distributed, across edge and cloud layers, but fundamental challenges related to semantic consistency, concept-drift management, reproducible assessments, and autonomous action governance persist. Another visible gap in the literature is the growing disconnect between the benchmark oriented systems research and domain level decision accountability. The field is also of major interest because the design of pipelines at present is dependent on the practical limitations of the autonomy itself: the lagging, the biasing or the patchy streams can disrupt the intelligence available downstream, whereas predictive models may appear robust in isolation while failing under pipeline-level constraints .