Enterprise Architecture for Scalable Cloud Native Systems Enabling Mobile Automation Machine Learning and Low Latency Telecom Services
Abstract
Enterprise architecture for scalable cloud-native systems is becoming critical for enabling modern mobile automation, machine learning-driven serverless ETL pipelines, and low-latency telecom services. By leveraging microservices, Kubernetes orchestration, serverless computing, and event-driven architectures, organizations can build highly resilient and elastic systems capable of handling dynamic workloads and real-time data streams. Machine learning models integrated into ETL workflows automate data transformation, anomaly detection, and predictive analytics, enhancing operational efficiency and decision-making.
Cloud-native principles support horizontal scaling, fault tolerance, and high availability, while API-led integration facilitates seamless connectivity between mobile applications, backend systems, and telecom service platforms. Intelligent observability, monitoring, and automated orchestration ensure service reliability and compliance at scale. This architecture empowers enterprises to deliver responsive, secure, and intelligent services in highly competitive mobile and telecom environments while maintaining flexibility for future innovation
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
15293-15300 |
Published |
December 10, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Sriram Subramanian (%2025). Enterprise Architecture for Scalable Cloud Native Systems Enabling Mobile Automation Machine Learning and Low Latency Telecom Services. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15293-15300. https://doi.org/10.15662/IJSRAT.2025.0806003 |
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