Multi Cloud Data Engineering Framework for Scalable Analytics and Generative AI Systems
Abstract
The exponential growth of data and the increasing adoption of Generative Artificial Intelligence (AI) have necessitated the development of robust, scalable, and flexible data engineering frameworks. Multi-cloud environments, which integrate services from multiple cloud providers, offer enhanced reliability, scalability, and vendor independence. This paper proposes a comprehensive multi-cloud data engineering framework designed to support scalable analytics and generative AI systems. The framework leverages distributed data pipelines, cloud-native architectures, and advanced orchestration techniques to ensure efficient data processing and model deployment. Generative AI models, such as large language models and diffusion models, require massive datasets and computational resources, which can be effectively managed through multi-cloud strategies. The study explores key components, including data ingestion, transformation, storage, and model lifecycle management, while addressing challenges such as data consistency, latency, and security. Additionally, the role of containerization, microservices, and DevOps practices in enabling seamless integration across cloud platforms is examined. The findings demonstrate that multi-cloud frameworks significantly improve scalability, fault tolerance, and performance, making them suitable for modern data-intensive applications and AI-driven systems.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
12827-12834 |
Published |
October 10, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Anthony Defede (%2024). Multi Cloud Data Engineering Framework for Scalable Analytics and Generative AI Systems. International Journal of Science, Research and Technology , Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 12827-12834. https://doi.org/10.15662/IJSRAT.2024.0705007 |
References
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2. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
3. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
4. Sampath Kumar Konda, “Fault-Tolerant BMS Modernization in Precision-Controlled Scientific Facilities: Zero- Downtime Migration Architectures”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 2, pp. 1223–1234, Mar. 2024, doi: 10.32628/CSEIT24102257.
5. Khan, M. F., & Hassan, M. M. (2024). Explainable Ai and Machine Learning Models for Transparent and Scalable Intrusion Detection Systems. J. Inf. Syst. Eng. Manag, 9(4s), 1576-1588.
6. Niture, N. A., & Abdellatif, I. (2020, October). Ai based airplane air pollution identification architecture using satellite imagery. In 2020 IEEE Cloud Summit (pp. 150-155). IEEE.
7. Guda, D. P. (2024). Cyber insurance for DevSecOps risks: Pricing models and coverage gaps. Journal of Information Systems Engineering and Management, 9(3).
8. Harish, M., & Selvaraj, S. K. (2023, August). Designing efficient streaming-data processing for intrusion avoidance and detection engines using entity selection and entity attribute approach. In AIP Conference Proceedings (Vol. 2790, No. 1, p. 020021). AIP Publishing LLC.
9. Sarabhu, V. B., & Balaji, V. (2018). Design and implementation for an improved version of cloud computing architecture by using concept of ontology with query retrieval and refinement mechanism. International Journal of Research and Applied Innovations (IJRAI), 1(1), 8–16.
10. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
11. Vijayakumar, R., & Gireesh, G. (2013, July). Quantitative analysis and fracture detection of pelvic bone X-ray images. In 2013 fourth international conference on computing, communications and networking technologies (ICCCNT) (pp. 1-7). IEEE.
12. 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.
13. Padala, S. (2023). Intelligent Workforce Management: A Predictive Analytics Approach. American International Journal of Computer Science and Technology, 5(3), 42-47.
14. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
15. Thumala, Srinivasarao. "Building Highly Resilient Architectures in the Cloud." Nanotechnology Perceptions 16.2 (2020).
16. Rajasekharan, R. (2017). The role of DevOps automation in improving enterprise database reliability. International Journal of Humanities and Information Technology (IJHIT), 2(1), 20–29.
17. Mangukiya, M. (2023). Blockchain-Enabled Traceability and Compliance in Global Electronics Production Networks. International Journal of Computer Technology and Electronics Communication, 6(6), 7999-8004.
18. Meka, S. (2023). Empowering Members: Launching Risk-Aware Overdraft Systems to Enhance Financial Resilience. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7517-7525.
19. 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.
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22. Sanepalli, Uttama Reddy. (2023). Distributed Multi-Cloud Data Lake Architecture for Enterprise-Scale Workplace Benefits Analytics: A Federated Approach to Heterogeneous Financial Data Integration. International Journal of Computer Engineering and Technology (IJCET), 14(1), 268-282.
23. Mudunuri, P. R. (2023). Automation-Driven Reliability Engineering for Public-Sector Biomedical Systems. International Journal of Humanities and Information Technology, 5(01), 68-86.
24. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011.
25. Rasul, I., Tohfa, N. A., Rahman, M., Hossain, I., Zareen, S., & Shakhawat, M. (2023). Quantum Machine Learning for Early Disease Diagnosis: A Systematic Review and Public Health Innovation Perspective, World Journal of Advanced Research and Reviews, 2023, 19(01), 1668-1674
26. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
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31. Kotla, M. R. T. (2023). AI in consumer digital banking: Enabling smart personalization and fraud detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 262–276.
32. Katta, T. B. (2023). Bridging MLOps and iPaaS: A Unified Framework for Governance and Observability in AI-Augmented Enterprise Integration. International Journal of Science, Research and Technology, 6(6), 11080-11084.
33. Gajula, S. (2023). A Review of Anomaly Identification in Finance Frauds using Machine Learning System. International Journal of Current Engineering and Technology, 13(06).
34. Kavuri, S. (2023). Machine learning approaches for security vulnerability detection in software testing. Computer Fraud & Security, 21-31.
35. Shewale, V. (2023). Operationalizing NIST CSF 2.0 and TSA Security Directives in Pipeline Cybersecurity. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9773-9779.
36. Parasa, M. (2020). Control-mapped AI governance for high-risk HR decisions in SAP SuccessFactors: Audit-ready metrics for recruiting, performance calibration, and internal mobility. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12(2), 153–168. https://doi.org/10.18090/samriddhi.v12i02.15
37. Subramanyam, S. P. (2023). Secure identity and access management frameworks for cloud native DevOps systems. International Journal of Computer Technology and Electronics Communication, 6(4), 7357–7366.
38. Namdeo, A. (2023). Neuromorphic edge analytics for industrial IoT. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8113–8123.
39. Sarabu, V. B. (2018). Architecting Financially Compliant Enterprise Point-of-Sale Systems: A Scalable Data Integrity and Revenue Recognition Framework for Global Retail Platforms. International Journal of Computer Technology and Electronics Communication, 1(2), 329-341.
40. Panyala, V. R. (2022). Engineering event-driven microservices platforms for real-time data processing in cloud ecosystems. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 34–48.
41. Pasumarthi, H. (2023). Applying machine learning to high-volume banking platforms: From transaction data to predictive risk intelligence. International Journal of Computer Technology and Electronics Communication, 6(4), 7352–7356
42. Vayyasi, N. K. (2023). Retail fraud analytics using generative intelligence and Java cloud frameworks. International Journal of Science, Research and Technology, 6(4), 10324-10337.
43. Yashwanth, K., Adithya, N., Sivaraman, R., Janakiraman, S., & Rengarajan, A. (2021, July). Design and Development of Pipelined Computational Unit for High-Speed Processors. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
44. Vigenesh, M., Upadhyay, A. K., Murali, M. J., Seth, K., & Shinde, G. R. (2024, June). Exploring the Role of Visual Information in Mixed Media Creation. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
45. G. Sarraf, “Autonomous Ransomware Forensics: Advanced ML Techniques for Attack Attribution and Recovery,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 3, pp. 1377–1390, Jul. 2023, doi: 10.48175/IJARSCT-11978W