Design and Implementation of a Real-Time Data Processing Framework for High-Throughput Applications

EasyChair Preprint 15747

Abstract

The rapid growth of high-throughput applications, spanning fields such as bioinformatics, financial analytics, and industrial automation, demands innovative data processing frameworks capable of handling large volumes of data in real time. This paper presents the design and implementation of a scalable and efficient real-time data processing framework tailored for high-throughput environments. The proposed framework integrates cutting-edge technologies, including stream processing, parallel computing, and cloud-based solutions, to ensure low-latency and high-throughput performance. We explore key aspects of system architecture, data ingestion, processing pipelines, and real-time analytics, with a focus on scalability, fault tolerance, and real-time decision-making capabilities. Additionally, we demonstrate the framework's application in a case study, showcasing its effectiveness in a large-scale high-throughput scenario. Performance evaluation results highlight significant improvements in processing efficiency and system responsiveness compared to traditional approaches. This framework offers a robust solution for applications requiring immediate insights from vast datasets, enabling more informed decision-making and operational efficiency.

Keyphrases: High-Throughput Application, parallel computing, real-time data processing, stream processing

@booklet{EasyChair:15747,
  author    = {Chris Johnson and Darall Smith and Sunday Oladele and Micheal Shange},
  title     = {Design and Implementation of a Real-Time Data Processing Framework for High-Throughput Applications},
  howpublished = {EasyChair Preprint 15747},
  year      = {EasyChair, 2025}}