A Scalable Microservices Architecture for Real-Time Data Processing in Cloud-Based Applications

Abstract: In today’s data-intensive landscape, the exponential growth of digital applications and IoT devices has heightened the demand for real-time data processing within cloud-native environments. Traditional monolithic systems struggle to meet the low-latency, high-availability requirements of modern workloads, prompting a shift toward microservices architectures. However, existing microservices-based approaches face persistent challenges, including inter-service communication latency, data consistency issues, limited observability, and complex orchestration—particularly under dynamic, real-time conditions. Addressing these gaps, this research proposes a novel, scalable microservices architecture optimized for real-time data processing using a modular, event-driven design. The task is to develop a strong and flexible system that will be able to consume real-time information on weather based on the data availed by the OpenWeatherMap application program interface, with the least latency and the utmost scalability. It incorporates the use of Apache Kafka, Apache Flink, Redis, Kubernetes, and adaptable autoscaling via KEDA and HPA in the architecture. It reduces inter-service communication latency by 25%, ensures data consistency under dynamic workloads, improves observability for faster issue detection, and enhances fault tolerance and throughput, demonstrating up to 40% faster processing in high-load real-time scenarios. Major building blocks are microservices built on Docker, orchestration on Kubernetes, an API gateway to route and secure traffic, a CI/CD pipeline to do fast deployments, and a distributed tracing observability stack of Prometheus, ELK, and Jaeger. Detailed analysis reports revealed that high-load systems were much more responsive, more fault-tolerant, and high-throughput experiments. Its proposed framework is dynamic work load management, automatic fault healing, and intelligent scaling, and hence minimizes the exposures of downsides and maintains a steady performance. To sum up, this study offers a tenable microservices design, addressing the present limitations in the field of real-time data processing and, at the same time, providing a scalable, secure, and observable architecture of future cloud native apps.

Desidi Narsimha Reddy, Rahul Suryodai, Vinay Kumar S. B, M. Ambika, Elangovan Muniyandy, V. Rama Krishna and Bobonazarov Abdurasul. “A Scalable Microservices Architecture for Real-Time Data Processing in Cloud-Based Applications”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.9 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160905

@article{Reddy2025,
title = {A Scalable Microservices Architecture for Real-Time Data Processing in Cloud-Based Applications},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160905},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160905},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {9},
author = {Desidi Narsimha Reddy and Rahul Suryodai and Vinay Kumar S. B and M. Ambika and Elangovan Muniyandy and V. Rama Krishna and Bobonazarov Abdurasul}
}