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Event-Driven Data Pipelines : A Cloud-Based Approach to Real-Time Data Processing

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  • Venkata Nagendra Kumar Kundavaram

Abstract

This article comprehensively analyzes event-driven data pipelines in cloud computing environments, examining their architecture, implementation considerations, and real-world applications. The article explores the fundamental components of event-driven systems, from event sources and message brokers to processing layers, while evaluating their performance characteristics and reliability mechanisms. Through detailed analysis of system architectures, we investigate the integration of various cloud services and their role in enabling scalable, real-time data processing. The article demonstrates how modern event-driven architectures achieve sub-millisecond processing times and handle millions of events per second while maintaining system resilience. Our findings reveal significant improvements in operational efficiency across various industries, including financial services, marketing automation, and IoT monitoring solutions. The article also addresses critical implementation aspects, including security frameworks, integration protocols, and best practices for system optimization. These insights provide valuable guidance for organizations leveraging event-driven architectures in their digital transformation initiatives. The article concludes that event-driven data pipelines represent a crucial advancement in data processing technology, offering unprecedented capabilities in handling real-time data streams while maintaining scalability, reliability, and cost-effectiveness.

Suggested Citation

  • Venkata Nagendra Kumar Kundavaram, 2024. "Event-Driven Data Pipelines : A Cloud-Based Approach to Real-Time Data Processing," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(6), pages 364-369, November.
  • Handle: RePEc:jbh:ijsrcs:v10:y2024:i6:id:424
    DOI: 10.32628/CSEIT24106183
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24106183
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