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Edge-Powered Big Data Analytics: Low-Latency Processing for IoT and Real-Time Systems

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  Introduction The proliferation of Internet of Things (IoT) devices and real-time applications has led to an explosion of data generated at the network's edge. Traditional cloud-based big data analytics, where data is sent to centralized servers for processing, often introduces significant latency, bandwidth constraints, and privacy concerns. Edge computing addresses these challenges by processing data closer to its source, enabling faster decision-making and efficient resource utilization. This chapter explores the role of edge computing in big data analytics, focusing on its application in IoT and real-time systems, architectural frameworks, benefits, challenges, and implementation strategies. Understanding Edge Computing in Big Data Analytics What is Edge Computing? Edge computing refers to the decentralized processing of data at or near the source of data generation, such as IoT devices, sensors, or edge servers, rather than relying solely on centralized cloud infrastructu...