Architecting Reliable Data Systems for Smart Agriculture: A Big Data and SRE Perspective

Bharat Nagamalla

doi:10.32628/CSEIT25111253

Authors

Bharat Nagamalla University of Central Missouri, USA Author

DOI:

https://doi.org/10.32628/CSEIT25111253

Keywords:

Smart Agriculture, Site Reliability Engineering (SRE), Big Data Analytics, IoT Sensor Networks, Precision Farming

Abstract

This article explores the transformation of agricultural systems through the integration of Big Data analytics and Site Reliability Engineering (SRE) practices, focusing on the development of robust and reliable data systems for smart agriculture. The article examines the evolution from traditional farming methods to data-driven precision agriculture, highlighting the critical role of IoT sensor networks, real-time analytics, and automated decision support systems. The article investigates the infrastructure requirements, challenges, and solutions in implementing reliable agricultural technology systems, including data collection mechanisms, processing architectures, and rural connectivity solutions. It addresses the importance of SRE practices in maintaining system reliability, incident response, and disaster recovery strategies while examining the implementation of predictive modeling and machine learning applications for crop management. The article also analyzes technical challenges in rural environments, data quality validation, system redundancy, and scalability requirements during peak agricultural seasons. Furthermore, it explores emerging trends and best practices in agricultural technology, emphasizing the importance of sustainable practices and cross-functional team structures in modern farming operations.

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