Abstract

Real-time stream processing architectures are pivotal for handling the continuous influx of data in big data applications within cloud environments. This paper presents an exploration of real-time stream processing architectures tailored for big data applications in cloud environments. We delve into the fundamental principles and components of stream processing systems, including data ingestion, processing, and output mechanisms. Additionally, we survey various stream processing frameworks and technologies specifically designed for cloud environments, assessing their scalability, fault-tolerance, and integration capabilities. Through empirical evaluation and case studies, we analyze the performance and suitability of different stream processing architectures for diverse big data application scenarios in cloud environments. Furthermore, we discuss emerging trends and challenges in real-time stream processing and propose potential avenues for future research and development. By synthesizing existing literature and practical insights, this study aims to provide guidance for practitioners and researchers in designing and deploying efficient and scalable real-time stream processing architectures for big data applications in cloud environments.

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