Abstract

In the era of big data, scalable data processing in cloud environments has emerged as a critical area of research and application. This study presents a comparative analysis of various approaches and technologies for achieving scalability in cloud-based data processing systems. We investigate different architectures, frameworks, and tools employed in modern cloud environments to handle large volumes of data efficiently. Through a systematic comparison, we evaluate the strengths, weaknesses, and performance characteristics of these approaches, considering factors such as scalability, reliability, cost-effectiveness, and ease of deployment. The findings of this comparative study provide valuable insights for researchers, practitioners, and organizations seeking to design, deploy, and optimize scalable data processing solutions in cloud environments.

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