Streaming-First Enterprise Decision Systems: Architectural Evolution from Batch Dataflows to Stateful, Exactly-Once Real-Time Processing
DOI:
https://doi.org/10.15662/IJEETR.2022.0401005Keywords:
Real-Time Data Streaming, Enterprise Decision Systems, Distributed Systems, Stream Processing, Lambda Architecture, Kappa Architecture, Fault Tolerance, Event-Time Processing, Exactly-Once Semantics, Distributed Messaging, CEP, MicroservicesAbstract
Enterprise decision systems increasingly depend on real-time data streams to enable operational intelligence, fraud detection, predictive maintenance, dynamic pricing, supply chain optimization, and adaptive customer engagement across digital platforms. The architectural evolution from batch-oriented distributed processing models to unified, stateful stream-processing engines has fundamentally reshaped how enterprises design, deploy, and scale mission-critical systems. Early distributed data systems such as Google’s MapReduce and Google File System established the principles of large-scale data partitioning, fault-tolerant execution, and horizontal scalability, forming the conceptual backbone for modern data infrastructure. Building upon these foundations, streaming platforms such as Apache Kafka introduced durable, distributed log-based messaging; Apache Spark advanced micro-batch stream computation; Apache Flink enabled true event-driven, stateful processing with consistent checkpointing; and Google’s MillWheel demonstrated low-latency, exactly-once semantics at Internet scale. Together, these innovations converged to form a cohesive architectural paradigm in which ingestion layers, stateful stream processors, scalable storage backends, and real-time serving components operate as an integrated decision fabric. By examining key architectural diagrams and seminal studies, this article synthesizes these developments into a unified blueprint for modern enterprise decision systems, highlighting core design principles for scalability, deterministic state management, event-time correctness, resilience under failure, elasticity under fluctuating workloads, and the practical realization of exactly-once processing guarantees in distributed environments.
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