Mastering Real-Time Data Processing Applications: Optimization Strategies for Peak Performance

Mohammed Naseer Khan

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 5, 895-904

Abstract: This comprehensive article explores strategies for optimizing real-time data processing applications in the era of big data and distributed systems. It examines the growing demand for real-time analytics across industries and organizations' challenges in maintaining low latency and scalability. The article discusses key optimization techniques, including leveraging the Akka framework's actor model and supervision strategies, implementing efficient data ingestion through stream processing and partitioning, utilizing caching strategies, employing robust monitoring and auto-scaling mechanisms, ensuring fault tolerance through checkpointing and graceful degradation, and adopting asynchronous communication patterns. Throughout the article, real-world case studies and performance metrics demonstrate the significant improvements these strategies can bring to system throughput, latency, and resilience in various sectors such as finance, e-commerce, telecommunications, and manufacturing.

Keywords: Real-Time Data Processing; Akka Framework; Caching Strategies; Fault Tolerance; Asynchronous Communication (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241051078
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i5:id:381

DOI: 10.32628/CSEIT241051078

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