Articles
Intelligent Cloud Data Engineering Frameworks for Secure Financial Healthcare and Business Systems
Abstract
Intelligent cloud data engineering frameworks have become a critical technological foundation for secure financial, healthcare, and business systems in the modern digital era. Organizations increasingly rely on cloud-enabled intelligent infrastructures to manage large-scale data processing, analytics, automation, and cybersecurity operations efficiently. This study explores the development and implementation of intelligent cloud data engineering frameworks that integrate cloud computing, artificial intelligence, machine learning, big data analytics, and cybersecurity technologies to support secure enterprise ecosystems. The research focuses on how intelligent cloud architectures improve data management, predictive analytics, operational efficiency, regulatory compliance, and secure computing environments across financial institutions, healthcare organizations, and business enterprises. The study also examines the role of distributed computing, hybrid cloud infrastructures, real-time analytics, data governance, and intelligent automation in optimizing enterprise data workflows and decision-making processes. A comprehensive literature review highlights recent advancements in cloud-based data engineering, AI-driven analytics, cybersecurity frameworks, and enterprise automation systems. The proposed research methodology introduces a multi-layered intelligent cloud framework integrating data engineering, analytics, governance, automation, and security services within a unified digital ecosystem. The findings indicate that intelligent cloud data engineering frameworks significantly improve enterprise scalability, cybersecurity resilience, predictive intelligence, operational transparency, and business continuity. However, challenges related to data privacy, interoperability, infrastructure complexity, and regulatory compliance continue to influence enterprise adoption and management strategies.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 7 No. 5 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
14915-14929 |
Published |
September 25, 2024 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Nadia Ben Azzouna (2024). Intelligent Cloud Data Engineering Frameworks for Secure Financial Healthcare and Business Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 5 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 14915-14929. https://doi.org/10.15662/IJAESIT.2024.0705008 |
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35. Balamuralidhar Sarabu, V. (2021). System-of-record governance in enterprise retail platforms: Architectural design principles for financial data ownership and consistency. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(2), 1–16.
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38. 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.
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2. Mallireddy, S. (2022). Business value of ServiceNow for health care and education services. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 191-196.
3. Soundappan, S. J. (2022). AI-Based Fault Detection and Isolation for Reliability in Modern Power Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106-7110.
4. Adepu, R. (2023). Zero trust architecture for large-scale enterprise infrastructure security. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 171–187.
5. Raja, G. V. (2022). Integrating network forensics with data mining for advanced cybercrime investigation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5321-5326.
6. Pasumarthi, H. (2023). Applying machine learning to high-volume banking platforms: From transaction data to predictive risk intelligence. International Journal of Artificial Intelligence & Machine Learning, 2(1), 356–370. https://doi.org/10.34218/IJAIML_02_01_029
7. Gangina, P. (2024). Intelligent Cost Optimization Strategies for Multi-Tenant SaaS Platforms Using Machine Learning. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 9976-9988.
8. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158-5168.
9. Murugeshwari, B., Jothi, D., Hemalatha, B., & Pari, S. N. (2023). Trust Aware Privacy Preserving Routing Protocol for Wireless Adhoc Network. arXiv preprint arXiv:2304.14653.
10. 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.
11. Subramani, V. (2023). Governance Led Security Architecture in Large Scale Enterprise Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9037-9045.
12. Balaji, K. V., & Sugumar, R. (2023, December). Harnessing the Power of Machine Learning for Diabetes Risk Assessment: A Promising Approach. In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (pp. 1-6). IEEE.
13. 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.
14. Yamsani, N. (2021). Governance by design: Secure role delegation and approval structures in enterprise master data systems. International Journal of Science, Engineering and Technology, 9(2). https://doi.org/10.5281/zenodo.18296977
15. 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
16. Vootla, A. (2023). Continuous Accessibility Assurance through DevSecOps-Integrated Testing Pipelines. International Journal of Research and Applied Innovations, 6(6), 9975-9984.
17. Mathew, A., & Alex, H. (2023). From Code to Cure: The Role of AI in Accelerating Drug Discovery. Advances and Challenges in Science and Technology Vol. 2, 94-102.
18. Lanka, S. (2022). Building smarter security systems with AI: Inside Citrix analytics for security. Journal of Advanced Research Engineering and Technology (JARET), 1(2), 93–109. https://doi.org/10.34218/JARET_01_02_009
19. Narayanan, S. (2024). Third-party AI vendor risk: Developing assessment frameworks for machine learning service providers. International Journal of Computer Science and Engineering and Information Technology, 10(4), 1133–1142. https://philarchive.org/archive/NARTAV
20. Ali, M., Hossain, M. S., Rahman, M. W., & Hossain, M. S. (2022). Leveraging Business Analytics to Enhance Supply Chain Resilience and Reduce Disruptions in Critical US Industries. Journal of Business and Management Studies, 4(4), 239-263.
21. Hema Latha Boddupally. (2020). EnterpriseScale Data Quality Improvement Using Machine Learning: Frameworks, Validation Strategies, and Operational Insights. European Journal of Advances in Engineering and Technology, 7(8), 138–149. https://doi.org/10.5281/zenodo.18083539
22. Parupalli, A. (2022). KPI-Driven Business Intelligence: A Review of Frameworks and Visualization Tools. Asian Journal of Computer Science Engineering, 7(4), 4.
23. Panyala, V. R. (2023). AI-augmented DevOps frameworks for accelerating cloud-native platform engineering at scale. International Journal of Research and Applied Innovations, 6(1), 8375–8379.
24. Myakala, P. K., & Naayini, P. (2023). Bridging the Gap: Leveraging Transfer Learning for Low-Resource NLP Tasks. International Journal of Computer Techniques, 10(5).
25. Kunadi, S. K. (2021). Establishing robust data foundations: Early-stage architecture for scalable data warehousing and analytics systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(3), 3078–3088.
26. Namdeo, A. (2024). Digital twin-driven predictive quality analytics. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7852–7862. https://doi.org/10.15662/IJEETR.2024.0602009
27. Thangaraj, S. J. J., Loganayagi, S., Vimal, V. R., Deepak, V., Banu, E. A., & Rani, J. P. A. (2023, August). Design of Internet Product Interface Based on Dynamic Model. In 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 92-97). IEEE.
28. Elminir, H. K., Sabbeh, S. F., ElSoud, M. A., & Gamal, A. (2012). Multi feature content based video retrieval using high level semantic concept. International Journal of Computer Science Issues (IJCSI), 9(4), 254.
29. Vayyasi, N. K. (2023). Optimizing factory maintenance and downtime prediction through Java-driven AI pipelines. International Journal of Research and Applied Innovations (IJRAI), 6(3).
30. Anbazhagan, K., Kumar, R., Thilagavathy, R., & Anuradha, D. (2024, March). Shortest Job First with Gateway-based Resource Management Strategy for Fog Enabled Cloud Computing. In 2024 4th International Conference on Data Engineering and Communication Systems (ICDECS) (pp. 1-6). IEEE.
31. Kale, P. (2024). A Deep Learning-Based Platform Engineering Framework for Predictive CI/CD Pipeline Optimization and Developer Productivity Enhancement. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(2), 194-202.
32. Nijaguna, G.S.; Manjunath, D.R.; Abouhawwash, M.; Askar, S.S.; Basha, D.K.; Sengupta, J. Deep Learning-Based Improved WCM Technique for Soil Moisture Retrieval with Satellite Images. Remote Sens. 2023, 15, 2005.
33. Adepu, G. (2023). Intelligent digital government platforms: Leveraging machine learning and cloud architecture for social service delivery. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 75–92.
34. 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.
35. Balamuralidhar Sarabu, V. (2021). System-of-record governance in enterprise retail platforms: Architectural design principles for financial data ownership and consistency. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(2), 1–16.
36. Macha, Y., & Pulichikkunnu, S. K. (2023). An Explainable AI System for Fraud Identification in Insurance Claims via Machine-Learning Methods. Int. J. Adv. Res. Sci. Commun. Technol, 3(3), 1391-1400.
37. Suvvari, S. K. (2023). Shift Left: Moving the Inclusion of Accessibility Functionalities to the Left in Agile Product Development Life Cycle. Journal of Computational Analysis and Applications, 31(4).
38. 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.
39. Prasad, P. K. (2024). AI-driven cloud governance 2.0: Balancing agility, compliance, and operational efficiency in hybrid multi-cloud environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7848–7851.
40. Vankayala, S. C. (2023). Observability-Driven QA for Serverless and PaaS Architectures: A Trace-Informed, SLO-Oriented Benchmarking Framework. International Journal of Science, Engineering and Technology, 11(5).