A Novel Architecture for Scaling Distributed Data Systems: Analysis of High-Throughput, Low-Latency Transaction Processing

Authors

  • Arun Kumar Sandu

Keywords:

distributed systems, stream processing, real-time analytics, fault tolerance, scalability, consensus algorithms.

Abstract

Scaling distributed data systems to manage millions of transactions per second while ensuring sub-millisecond latency presents significant challenges in consistency, availability, and real-time analytics. This study explores the architectural innovations required to meet these demands,particularly focusing on multi-region active-active replication, event-driven data ingestion with Kafka, and high-performance analytics using Druid.

References

Bernstein, P. A., & Newcomer, E. (2009). Principles of transaction processing for the systems professional. Morgan Kaufmann Publishers.

Brewer, E. A. (2000). Towards robust distributed systems. ACM SIGACT News, 31(2), 7-10. https://doi.org/10.1145/351180.351189

Cao, X., Zhang, Y., & Guo, H. (2019). Scalable and consistent distributed data systems: A comprehensive survey. Journal of Computer Science and Technology, 34(4), 755-774. https://doi.org/10.1007/s11390-019-1914-4

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Published

2020-04-01

How to Cite

Arun Kumar Sandu. (2020). A Novel Architecture for Scaling Distributed Data Systems: Analysis of High-Throughput, Low-Latency Transaction Processing . Journal of Computational Analysis and Applications (JoCAAA), 28(4), 1–31. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2264

Issue

Vol. 28 No. 4 (2020): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.