Developing Frameworks for Real-Time Data Processing in Cloud Systems

Abstract

Real-time data processing in cloud systems is now an essential need in today's digital environment, fueled by the explosive increase in data produced by IoT devices, social media, and enterprise applications. This paper presents an extensive study of real-time data processing frameworks in cloud systems with emphasis on architectural paradigms, data processing engines, and system scalability.

Important challenges such as latency minimization, fault tolerance, and effective resource utilization are discussed along with suggested solutions. A new framework is presented, showing its efficiency through performance analysis and benchmarking against current systems. Results show enhanced processing speed, scalability, and resource utilization.