Articles
Reinventing Data Pipelines Intelligent Multi Cloud Engineering for Seamless Interoperability and Integration Frameworks
Abstract
The increasing complexity of modern data ecosystems has driven the need for advanced data pipeline architectures capable of operating seamlessly across multiple cloud environments. This paper presents a novel approach to reinventing data pipelines through intelligent multi-cloud engineering, focusing on achieving seamless interoperability and robust integration frameworks. The proposed system leverages Artificial Intelligence (AI) and automation to orchestrate data workflows across heterogeneous cloud platforms, enabling efficient data movement, transformation, and synchronization. By incorporating cloud-agnostic design principles, API-driven integrations, and containerized microservices, the framework ensures flexibility, scalability, and vendor independence. Advanced techniques such as metadata-driven processing, real-time stream analytics, and adaptive workload management are utilized to optimize pipeline performance and reliability. Furthermore, the architecture integrates security mechanisms, data governance policies, and compliance standards to ensure safe and trustworthy data operations across distributed environments. Experimental evaluations demonstrate that the intelligent multi-cloud pipeline significantly reduces latency, enhances data consistency, and improves system resilience compared to traditional single-cloud approaches. This research contributes to the development of next-generation data engineering frameworks that support dynamic, scalable, and interoperable enterprise data ecosystems in an increasingly distributed digital landscape.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 9 No. 1 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
43-52 |
Published |
February 13, 2026 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Julie G (2026). Reinventing Data Pipelines Intelligent Multi Cloud Engineering for Seamless Interoperability and Integration Frameworks. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 9 No. 1 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 43-52. https://doi.org/10.15662/IJAESIT.2026.0901006 |
References
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2. Thota, M. R. (2025). AI-native infrastructure for the autonomous enterprise: Advancing self-optimizing database, big data, and cloud ecosystems. International Journal of Scientific Research in Science and Technology, 12(14), 527–533. https://doi.org/10.32628/IJSRST25121450
3. Gopinathan, V. R. (2023). Cloud-First AI Security Architecture for Protecting Enterprise Digital Ecosystems and Financial Networks. International Journal of Research and Applied Innovations, 6(6), 10031-10039.
4. Anand, L. (2023). An Intelligent AI and ML–Driven Cloud Security Framework for Financial Workflows and Wastewater Analytics. International Journal of Humanities and Information Technology, 5(02), 87-94.
5. Varma, K. K., & Anand, L. (2025, March). Deep Learning Driven Proactive Auto Scaler for High-Quality Cloud Services. In International Conference on Computing and Communication Systems for Industrial Applications (pp. 329-338). Singapore: Springer Nature Singapore.
6. Kale, A. (2025). CAC Payback Period Optimization Through Automated Cohort Analysis. International Journal of Management and Business Development, 2(10), 15-20.
7. Kunadi, S. K. (2025). The Societal Impact of Data Democratization in Enterprise Revenue Systems. Journal of Computer Science and Technology Studies, 7(12), 214-222.
8. Grandhe, K. (2025). Impact of Real-Time Analytics on Strategic Decision-Making in Large Organizations. IJSAT-International Journal on Science and Technology, 16(4).
9. Nair, S. G. (2025). Designing Secure and Scalable Microservices for Threat Detection: Engineering Patterns from Endpoint Security Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 11200-11209.
10. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
11. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661
12. Kumar, S. A., & Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII Transactions on Internet and Information Systems, 19(11), 3841-3855.
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19. Parepalli, S. (2021). Mapping Critical Data Relationships to Enable Automated Evaluation of Operational Impact. J Artif Intell Mach Learn & Data Sci, 1(1), 3175-3184.
20. Jaikrishna, G., & Rajendran, S. (2020). Cost-effective privacy preserving of intermediate data using group search optimisation algorithm. International Journal of Business Information Systems, 35(2), 132-151.
21. Ireddy, R. K. (2024). Event-native financial onboarding platforms: A Kafka-centric reference architecture for sub-minute identity and compliance processing. World Journal of Advanced Research and Reviews, 21(2), 2182–2192. https://doi.org/10.30574/wjarr.2024.21.2.0448
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23. Yasin, M., Rahman, M. B., Kanojiya, S., & Hasan, M. (2025). Strategic decision-making in healthcare using advanced business analytics techniques. Nvpubhouse Library for International Journal of Medical Science and Public Health Research, 6(10), 163-190.
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28. Karthikeyan, K., Umasankar, P., Parathraju, P., Prabha, M., & Pulivarthy, P. Integration and Analysis of Solar Vertical Axis Wind Hybrid Energy System using Modified Zeta Converter.
29. Aashiq Banu, S., Sucharita, M. S., Soundarya, Y. L., Nithya, L., Dhivya, R., & Rengarajan, A. (2020). Robust Image Encryption in Transform Domain Using Duo Chaotic Maps—A Secure Communication. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 271-281). Singapore: Springer Singapore.
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32. Barigidad, S., Hameed, S., Karri, N., Jangam, S. K., Pedda, P. S. R., & Gupta, D. (2025, December). Computational Modeling of AI-Enhanced Learning Pathways: A Mathematical Framework for Optimizing Knowledge Acquisition, Cognitive Load Management, and Student Performance in STEM Education. In 2025 International Conference on AI-Driven STEM Education and Learning Technologies (AISTEMEDU) (pp. 1-7). IEEE.
33. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
34. Pothireddy, S. R. (2025). An efficient and secure data sharing scheme for edge-enabled IoT. International Journal of Advances in Engineering and Management (IJAEM), 7(1), 597–603. https://ijaem.net/issue_dcp/An%20Efficient%20and%20Secure%20Data%20Sharing%20Scheme%20for%20Edge%20Enabled%20IoT.pdf
35. Yamsani, N. (2024). Large Language Models for Intelligent Data Stewardship in Enterprises: Architectures, Provenance, and Evidence-Mapped Governance. International Journal of Computer Technology and Electronics Communication, 7(1), 8210-8219.
36. Vimal Raja, G. (2024). Intelligent Data Transition in Automotive Manufacturing Systems Using Machine Learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
37. Mogili, V. B. (2024). Design and evaluation of secure healthcare applications built on Microsoft Power Platform. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10534-10545.
38.Ranjith Rajasekharan. (2019). Hybrid cloud architecture for enterprise database system. International Journal of Science, Research and Technology (IJSRAT), 2(6), 2513–251.