Integrating Cloud-Native Microservices with SAP Enterprise Platforms Through Event-Driven Architecture
Abstract
The integration of cloud-native microservices with SAP enterprise platforms through event-driven architecture provides a scalable and flexible approach to modernizing enterprise information systems. Traditional SAP environments often rely on tightly coupled applications, synchronous interfaces, and centralized processing mechanisms that can limit agility, interoperability, and responsiveness. Cloud-native microservices address these limitations by decomposing business capabilities into independently deployable services, while event-driven architecture enables asynchronous communication through events, message brokers, and distributed event-processing mechanisms. This research explores an integrated framework that connects SAP enterprise platforms, including SAP S/4HANA and SAP Business Technology Platform, with cloud-native services through event-based communication. The proposed methodology incorporates event producers, event brokers, consumer services, API management, schema validation, monitoring, and security enforcement to support reliable enterprise integration. The framework emphasizes scalability, fault tolerance, data consistency, interoperability, and operational observability across distributed environments. It also examines the role of event filtering, retry mechanisms, idempotent processing, dead-letter queues, and distributed tracing in maintaining dependable information exchange. Evaluation criteria include event-processing latency, throughput, delivery reliability, resource utilization, and integration complexity. The proposed approach aims to improve business-process responsiveness, reduce application coupling, and support incremental modernization without requiring complete replacement of existing SAP investments. The framework offers a foundation for resilient, secure, and maintainable enterprise integration across hybrid and multi-cloud environments.Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 9 No. 4 (2026): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
1388-1396 |
Published |
July 13, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr. Vuda Sreenivasarao (%2026). Integrating Cloud-Native Microservices with SAP Enterprise Platforms Through Event-Driven Architecture. International Journal of Science, Research and Technology , Vol. 9 No. 4 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 1388-1396. https://doi.org/10.15662/IJSRAT.2026.0904009 |
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2. Tarigoppula, S. (2024). Autonomous cloud operations: Integrating AIOps with AWS infrastructure for predictive incident prevention. International Journal of Engineering & Extended Technologies Research, 6(2), 7956–7967.
3. Gopinathan, V. R. (2022). Advanced Predictive Decision Intelligence Using Machine Learning for Strategic Enterprise Management Platforms. International Journal of Research and Applied Innovations, 5(6), 8162-8169.
4. Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231-244). Singapore: Springer Nature Singapore.
5. Kalabhavi, V. (2024). Promotional spend analytics using AI for PFME allocation and coupon redemption optimization. International Journal of Emerging Trends in Engineering and Management Research, 9(2), 15394–15406.
6. Chandran, A. S. (2026). Towards real-time cohort analytics at petabyte scale: A configuration-driven architecture for healthcare claims data. International Journal of Science, Research and Technology, 9(2), 467–476.
7. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
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9. Ramasamy, M. (2023). Cloud-native control plane design for modern network automation systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9082–9091.
10. Kundavaram, R. R., & Onteddu, A. R. (2023). Enhancing automotive performance and real-time data analytics through cloud computing. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1382–1392.
11. Chinthamani, R. (2025). Policy-as-code for automated enterprise production stability and deployment governance. International Journal of Computer Technology and Electronics Communication, 8(2), 10503–10508.
12. Polamarasetty, V. K. (2021). Modernizing SAP sales and distribution systems through ABAP-based enterprise solutions. International Journal of Research and Applied Innovations, 4(2), 4925-4930.
13. Bitragunta, S. L. V., & Paramasivan, M. (2023). Midterm dynamic simulation for the governance of reserves in systems with elevated renewable energy integration. Journal of Artificial Intelligence, 1(1), 1956–1962.
14. Shaik, N. (2025). Dynamic healthcare intelligence: Integrating AI predictive analytics with Kubernetes scaling for enhanced patient outcomes. International Journal of Engineering & Extended Technologies Research, 7(2), 9715–9720.
15. Seknametla, P. R., & Sunkara, R. (2025). Applying AIOps for Predictive Incident Management in DevOps-Driven Cloud Infrastructure. International Journal, 12(6).
16. Mysula, S. (2023). Performance optimization of distributed enterprise integration workflows across heterogeneous applications. International Journal of Engineering & Extended Technologies Research, 5(1), 6003–6008.
17. Ahuja, D. (2026, April). Declarative Multi-Cluster Kubernetes Deployment Architecture Using GitOps. In 2026 International Symposium of Systems, Advanced Technologies and Knowledge (ISSATK) (pp. 1-6). IEEE.
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27. Shaik, N. (2023). Zero downtime, zero trust: Automating certificate security at scale. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 8(4), 13973–13988.
28. Madabathula, L. (2025). A governance-driven enterprise lakehouse architecture for real-time analytics and machine learning enablement. International Journal of Applied Mathematics, 38(12s), 5022–5033.
29. Parupalli, A. (2025, August). Improving Cloud Resource Utilization and Cost Efficiency Through Data-Driven ML Algorithms. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1-8). IEEE.
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31. Ahuja, D. (2026, April). Declarative Multi-Cluster Kubernetes Deployment Architecture Using GitOps. In 2026 International Symposium of Systems, Advanced Technologies and Knowledge (ISSATK) (pp. 1-6). IEEE.
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