A Scalable Cloud Computing Architecture for Real-Time Agricultural Data Management and Analytics

Modern agriculture faces unprecedented challenges in managing and processing vast amounts of heterogeneous data generated by IoT sensors, satellite imagery, and weather monitoring systems. This paper presents a novel scalable cloud computing architecture specifically designed for real-time agricultural data management and analytics. Our proposed framework addresses critical issues including data heterogeneity, scalability limitations, and real-time processing requirements inherent in precision agriculture applications. The architecture leverages containerized microservices, distributed data processing pipelines, and elastic resource allocation mechanisms to handle variable agricultural workloads. Performance evaluation demonstrates that our system achieves 99.2% uptime with sub-200ms response times for real-time queries while supporting up to 10,000 concurrent sensor nodes. The architecture successfully reduces data processing costs by 34% compared to traditional monolithic approaches while improving analytical accuracy by 18% through enhanced data integration capabilities.