CLOUD PLATFORMS AS THE KEY TO EFFECTIVE STORAGE AND ANALYSIS OF IOT DATA

Abstract

The rapid development of the Internet of Things (IoT) has led to a significant increase in the volume of data generated by devices, sensors, and automated systems. This creates a demand for high-performance solutions capable of storing, processing, and analyzing data in real time. Traditional on-premises infrastructures have proven to be inflexible and costly, prompting organizations to migrate toward cloud platforms that provide scalability, reliability, and seamless integration with machine learning tools. The article analyzes the key capabilities of major cloud providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) in the context of IoT solution deployment. It examines their services for device connectivity, data streaming, analytics, security, and visualization. A comparative assessment of these platforms based on scalability, integration, security, analytical capabilities, and cost-efficiency shows that AWS and Azure demonstrate the highest overall performance (8.375 points), while GCP stands out for its analytical speed enabled by BigQuery and Dataflow. Based on the analysis, a universal model for deploying IoT solutions in a cloud environment is proposed. The model includes the stages of device connectivity, data transmission, storage, stream processing, analytics, and visualization. A practical example of an industrial monitoring system demonstrates how the combination of cloud services and machine learning algorithms enables anomaly detection in equipment operation and prevents production downtime. The results confirm that cloud technologies are a key factor in enhancing the efficiency of IoT systems, providing an optimal balance between scalability, security, and cost-effectiveness. The proposed approach can be applied to the design of flexible and secure IoT solutions across various sectors, including industry, transportation, energy, and smart cities.