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Event-Driven Cloud Data Integration for Enterprise Systems Using Stream Processing and Distributed Transaction Management

Dr. Meiyu Lu

Department of Computer Science, National University of Singapore, Singapore, Singapore

Abstract

Event-driven cloud data integration has emerged as an effective approach for connecting heterogeneous enterprise systems, enabling real-time information exchange, scalable data processing, and reliable coordination of distributed business operations. Traditional integration architectures often depend on batch processing, centralized middleware, and tightly coupled communication patterns, which can introduce latency, operational complexity, and consistency challenges. This research examines an event-driven cloud data integration framework that combines stream processing, distributed transaction management, and asynchronous messaging to improve interoperability across enterprise applications. The proposed framework integrates event producers, message brokers, stream processing engines, transaction coordination mechanisms, and cloud-based data repositories to support continuous data movement and dependable processing. The methodology emphasizes event ordering, idempotent execution, duplicate detection, failure recovery, transaction consistency, and observability. It also establishes an evaluation strategy based on processing latency, throughput, transaction completion rate, data consistency, recovery time, and resource utilization. The framework is designed to accommodate enterprise environments involving customer relationship management, enterprise resource planning, supply chain management, and financial information systems. Particular attention is given to the trade-offs between availability, scalability, and transactional guarantees in distributed cloud environments. The study provides a structured foundation for evaluating event-driven integration strategies and identifying operational conditions under which asynchronous processing can outperform conventional integration approaches. Its findings can guide enterprise architects in designing resilient, maintainable, and scalable cloud integration platforms for modern digital business operations.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 10 No. 6 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2025.1006013

Pages

18994-19002

Published

December 15, 2025
Copyright

Open Access

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

How to Cite

Dr. Meiyu Lu (%2025). Event-Driven Cloud Data Integration for Enterprise Systems Using Stream Processing and Distributed Transaction Management. International Journal of Emerging Trends in Engineering and Management Research , Vol. 10 No. 6 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 18994-19002. https://doi.org/10.15662/ijetemr.2025.1006013

References

1. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599–616. Elsevier. https://doi.org/10.1016/j.future.2008.12.001
2. 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.
3. Lenin, S. T., & Venkatasalam, K. (2025). Effective classification of heart disease using convolutional neural networks. Circuits, Systems, and Signal Processing, 44(2), 911-935.
4. Ahuja, D. (2025). DevOps and Ethical AI: Ensuring Responsible Deployment. Journal Of Multidisciplinary, 5(6), 1-14.
5. Shaik, N. (2022). Beyond vendor lock-in: Building intelligent multi-cloud Kubernetes orchestration. International Research Journal of Innovative Engineering, 6(2), 9985–9996.
6. Reddy, K. V. (2021). UVM methodology for industry-specific applications using AI in hardware verification. International Journal of Science, Research and Technology, 4(3), 5731–5744.
7. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
8. Chinnam, N. B. (2022). Engineering real-time supply chain visibility and ETA intelligence platforms for large-scale retail distribution networks. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7918–7922.
9. 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.
10. Basavala, S. R. (2022). Security-by-design: Threat modeling and requirements for enterprise applications. International Journal of Science, Research and Technology, 5(1), 7168–7178.
11. Gajjela, H., & Kari, M. (2024). Secure Multi-Tenant AI Architecture Enhances Enterprise AI Adoption Through Tenant Trust Using PLS-SEM in India’s Salesforce Cloud Industry. International Journal of Engineering Science & Humanities, 14(2), 253-271.
12. Srikanth, D. V. (2025). AI-powered entrepreneurial ecosystems: a computational model for sustainable startup growth in green tech sectors. Int. J. Environ. Sci, 1068-1077.
13. 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.
14. 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.
15. Gunasekaran, R. M. N. (2024). From Data to Decisions: How Quality Drives Machine Learning Success. The Eastasouth Journal of Information System and Computer Science, 2(01), 120-130.
16. Padmanabham, S. (2025). Security and Compliance in Integration Architectures: A Framework for Modern Enterprises. International Journal of Computing and Engineering, 7(16), 45-55.
17. Mysula, S. (2024). Event-driven integration architecture using publication methods for real-time DealCloud business operations & workflows. International Journal of Research and Applied Innovations, 7(4), 11233–11239.
18. Parupalli, A. (2024). Business-oriented employee performance assessment via machine learning in ERP systems. TIJER–International Research Journal, 11(11), a185–a192.
19. Gadige, C. D. (2023). Scalable CRM automation for high-volume customer onboarding: An event-driven Salesforce architecture. International Journal of Emerging Trends in Engineering and Management Research, 8(6), 14762–14772.
20. Bitragunta, S. L. V. (2024). Intelligent power feedback control for motor-generator pairs: A machine learning-based approach. International Journal of Leading Research Publication, 5(12), 1–9.
21. Neela, S. (2023). Resilient middleware ecosystems: Integrating fault tolerance, recovery-oriented design, and observability for real-time enterprise integration. International Journal of Emerging Research in Engineering and Technology, 4(1), 186–189. https://doi.org/10.63282/3050-922X.IJERET-V4I1P120
22. Chandran, A. S. (2024). Engineering billion-query search systems: Lessons from large-scale metadata platforms. International Journal of Research and Applied Innovations, 7(2), 10510–10519.
23. Karuturi, Y. (2023). Energy-aware capacity governance for large-scale SAP HANA deployments. International Journal of Research and Applied Innovations, 6(3), 6000–6009.
24. Jinna, K. R., & Bandari, U. (2023). Progressive delivery for machine-learning inference services: A comparative study of blue-green, canary and shadow strategies with model-aware gates on enterprise container platforms. International Journal of Research Publications in Engineering, Technology and Management, 6(4), 9092–9101.
25. Kopparthi, G. S. (2023). Trustworthy artificial intelligence for high-stakes decision-making: Explainability, human oversight, and system reliability. International Research Journal of Innovative Engineering, 7(4), 12847–12860.
26. Chandrashekar, P., & Kari, M. (2024). A Study on Artificial Intelligence in Software Engineering with Methodologies, Applications, and Effects on SDLC. TIJER–Int. Res. J, 11(12), 932-937.
27. Seknametla, P. R., & Sunkara, R. (2024). Threat Modeling Integration in DevSecOps Pipelines: Early-Stage Security Risk Identification Using Shift-Left Approaches. International Journal of Emerging Research in Engineering and Technology, 5(1), 126-133.
28. Singh, M. P. (2024). Building Cyber-Resilient Cloud Infrastructure Integrating Zero Trust, Identity Security, and Continuous Threat Monitoring. IACSE-International Journal of Cyber Security (IACSE-IJCS), 5(2), 7-23.
29. Uddandarao, D. P., Mahajan, A., & Krishnamsetty, P. (2024). Double machine learning for downstream causal measurement: A hybrid econometric-ML framework for feature valuation. International Journal of Computational and Experimental Science and Engineering, 10(4), 4477–4485.
30. Lingala, B. (2025). Transforming Enterprise Transaction Data into Intelligent Decision Systems. International Journal of Computer Technology and Electronics Communication, 8(5), 11488-11494.
31. 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.
32. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
33. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
34. 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.
35. Rengarajan, A. (2022). Developing AI Powered Enterprise Integration Platforms Through Cloud Native Observability and Automated Governance. International Journal of Science, Research and Technology, 5(6), 8970-8977.
36. Sugumar, R. (2022). Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis. International Journal of Emerging Trends in Engineering and Management Research, 7(5), 12528.