Edge versus Cloud: Evaluating Big Data Processing Paradigms for IoT Applications
Keywords:
Big data, Cloud computing, Data governance, DataCloud, Edge computing, Federated learning, Fog computing, Hybrid architecture, IoT, Latency, Real-time processing, TinyMLAbstract
The explosive growth of Internet of Things (IoT) ecosystems across industries, from factory floors and hospital wards to smart farms and urban infrastructure, has fundamentally changed how data processing is perceived. Billions of connected devices now generate continuous streams of sensor data, telemetry, images, and events, creating a need for computing architectures that are fast, scalable, secure, and cost-effective. Two paradigms dominate today's deployments: edge computing, which processes data close to where it is generated, and cloud computing, which centralises massive computational power in global data centres. This study offers a structured comparison of both paradigms in the context of IoT big data. The study introduce a two-category taxonomy: Type 1 workloads requiring millisecond-level responses, and Type 2 workloads suited for large-scale batch analytics and argue that the optimal architecture depends heavily on the workload’s latency profile, privacy requirements, scale, and operational context. The analysis covers architecture, performance, security, cost, and governance dimensions, and is grounded in real-world case studies across industrial automation, smart cities, precision agriculture, and healthcare. The study concludes that a thoughtfully designed hybrid architecture combining edge autonomy with cloud depth is the most effective path forward for most production IoT systems.
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