Cloud-Based Data Analytics Models for Scalable Computing Systems

Peter Sloot

Cloud-Based Data Analytics Models for Scalable Computing Systems

Author(s)	Prof. Peter Sloot
Country	Netherlands
Abstract	The rapid growth of digital information has created substantial demand for computing infrastructures capable of storing, processing, and analyzing large and continuously generated datasets. Conventional computing environments may experience limitations when workloads increase rapidly or require real-time analytical processing. Cloud computing provides an elastic and distributed infrastructure through which organizations can dynamically access computing, storage, networking, and analytical resources. This study examines cloud-based data analytics models for scalable computing systems and develops a conceptual framework connecting cloud infrastructure, distributed data processing, analytical models, resource management, scalability, and intelligent decision-making. A conceptual and descriptive research design is adopted through a structured synthesis of secondary scholarly literature concerning cloud computing, Big Data Analytics, distributed computing, cloud-native architectures, machine learning, stream processing, serverless computing, and scalable data management. Existing research indicates that cloud environments can provide elasticity, flexible resource allocation, and scalable processing capabilities for large-scale analytics workloads. Cloud-supported Big Data Analytics has been increasingly associated with distributed processing frameworks, real-time analytics, data-intensive applications, and machine-learning workloads. The study analyzes major cloud-based analytics models, including Infrastructure-as-a-Service, Platform-as-a-Service, serverless computing, cloud-native distributed processing, and hybrid cloud architectures. The analysis identifies scalability, processing performance, cost efficiency, fault tolerance, data security, privacy, interoperability, resource utilization, and workload variability as major factors influencing model effectiveness. The findings suggest that scalable cloud analytics requires an integrated architecture in which data storage, processing frameworks, analytical algorithms, and resource management mechanisms operate cohesively. Recent research further indicates that cloud-native and distributed frameworks can address growing data volumes through parallel processing, elastic resource allocation, containerization, and event-driven architectures. The study concludes that cloud-based data analytics can provide a flexible foundation for scalable computing systems when organizations appropriately match architectural models with workload characteristics, analytical requirements, security obligations, and cost constraints.
Keywords	Cloud Computing, Data Analytics, Scalable Computing, Big Data Analytics, Cloud-Native Architecture, Distributed Computing, Serverless Computing, Machine Learning, Data Processing, Elastic Computing
Field	Engineering
Published In	Volume 2, Issue 1, January-February 2020
Published On	2020-02-29

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Cloud-Based Data Analytics Models for Scalable Computing Systems

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