Abstract

In today’s hyper-competitive and data-driven economy, organizations require instantaneous insights to drive strategic decisions. Real-time streaming analytics (RTSA) has emerged as a transformative paradigm, enabling the continuous ingestion, processing, and analysis of high-velocity data streams from heterogeneous sources. This paper investigates the core technologies underpinning RTSA—such as Apache Kafka, Spark Streaming, Flink, and cloud-native event processing architectures—while exploring the integration of artificial intelligence and machine learning models for predictive insights. Drawing on cross-industry use cases spanning finance, manufacturing, healthcare, and logistics, the study demonstrates how RTSA supports fraud detection, dynamic pricing, customer personalization, and operational optimization. Particular emphasis is placed on evaluating latency constraints, scalability trade-offs, and governance requirements across regulated environments. Moreover, this review critically assesses the alignment of streaming analytics frameworks with enterprise objectives such as compliance, agility, and data sovereignty. Through the synthesis of over 80 peer-reviewed sources, this study presents a forward-looking perspective on the convergence of RTSA with edge computing, federated learning, and autonomous decision systems. The paper concludes by proposing a hybrid architecture for scalable real-time decision-making that balances throughput, interpretability, and strategic foresight in next-generation business ecosystems.