Cloud-Centric IoT Data Processing: A Multi-Platform Approach Using AWS, Azure, and Snowflake
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I1P102Keywords:
IoT, multi-platform architecture, machine learning, real-time analytics, edge computing, data security, cloud computing, predictive maintenance, interoperability, scalabilityAbstract
The Internet of Things (IoT) has revolutionized the way data is collected, processed, and analyzed. With the exponential growth of IoT devices, the volume of data generated is overwhelming, necessitating robust and scalable data processing solutions. This paper explores a cloud-centric approach to IoT data processing using a multi-platform architecture that leverages AWS, Azure, and Snowflake. The proposed architecture aims to address the challenges of data ingestion, storage, processing, and analytics in a distributed and efficient manner. We present a detailed evaluation of the performance and cost-effectiveness of the proposed system, supported by empirical data and case studies. The paper also discusses the integration of machine learning and real-time analytics to enhance the value of IoT data. Finally, we provide recommendations for future research and development in the field of cloud-centric IoT data processing
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