Machine Learning–Enabled Enterprise Lakehouse and Multi-Cloud Data Architecture for Scalable Financial and Business Intelligence Systems
DOI:
https://doi.org/10.15662/IJRAI.2025.0804017Keywords:
Machine Learning, Enterprise Lakehouse, Multi-Cloud Architecture, Financial Analytics, Business Intelligence, Big Data, Data Governance, Scalable Data Systems, Cloud ComputingAbstract
In the era of digital transformation, organizations generate massive volumes of structured and unstructured data from financial systems, enterprise applications, customer interactions, and external platforms. Traditional data warehouse architectures often struggle to handle the increasing complexity, scalability requirements, and real-time analytics demands of modern enterprises. The emergence of data lakehouse architectures and multi-cloud infrastructures has created new opportunities for building scalable and intelligent data ecosystems. This research explores a machine learning–enabled enterprise lakehouse architecture integrated with multi-cloud environments to support scalable financial analytics and business intelligence systems.
The proposed architecture combines the flexibility of data lakes with the structured performance capabilities of data warehouses, enabling efficient storage, processing, and analytics of large-scale enterprise data. Machine learning models are integrated within the architecture to automate data analysis, detect patterns, predict financial trends, and support strategic decision-making. The multi-cloud approach ensures high availability, scalability, and vendor flexibility by distributing workloads across multiple cloud platforms.
Additionally, the architecture emphasizes secure data governance, data quality management, and real-time analytics capabilities required for enterprise financial operations. The study highlights the benefits of integrating machine learning with lakehouse platforms for advanced analytics while addressing challenges related to data integration, infrastructure complexity, and operational costs.
References
1. Ganesan, G. B. K. (2024). A Zero-Trust Enterprise Integration Reference Architecture for Regulated Industries. International Journal of Research and Applied Innovations, 7(4), 11086–11095.
2. Suddala, V. R. A. K. (2024). Driving innovation and compliance in global payment platforms through predictive analytics and DevOps automation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10662–10672.
3. Sheta, S. V. (2023). The importance of software documentation in the development and maintenance phases. REDVET - Revista Electrónica de Veterinaria, 24(3), 609–618.
4. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
5. Kamadi, S. (2025). Machine learning and AI architecture: A comprehensive framework for production-grade intelligent systems. World Journal of Advanced Research and Reviews, 27(1), 2789–2799. https://doi.org/10.30574/wjarr.2025.27.1.2654
6. Kiran, A., & Kumar, S. (2024). A methodology and an empirical analysis to determine the most suitable synthetic data generator. IEEE Access, 12, 12209–12228.
7. Gowda, M. K. S. (2024). Leveraging machine learning to enhance accuracy and efficiency in regulatory compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10683–10692.
8. Adari, V. K. (2024). How cloud computing is facilitating interoperability in banking and finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465–11471.
9. Jagadeesh, S., & Sugumar, R. (2017). A comparative study on artificial bee colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243–248.
10. Karnam, A. (2024). Next-gen observability for SAP: How Azure Monitor enables predictive and autonomous operations. International Journal of Computer Technology and Electronics Communication, 7(2), 8515–8524. https://doi.org/10.15680/IJCTECE.2024.0702006
11. Ambati, K. C. (2025). An event-driven architecture for autonomous supply chain risk detection and decision automation. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(1), 1202–1211.
12. Raju, S., & Sindhuja, D. (2024). Transparent encryption for external storage media with mobile-compatible key management by Crypto Ciphershield. PatternIQ Mining, 1(3), 12–24.
13. Sridevi, V., Azath, H., Vijayakumar, R., Anbuselvan, N., Amirthalingam, V., & Arunkumar, S. (2024, April). Augmented reality shopping and IoT-enabled virtual try-on with cloud services for interactive product displays. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 880–885). IEEE.
14. Ireddy, R. K. (2024). Deep learning architecture for banking risk management: Cloud and AI-driven predictive analytics solution. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/CSEIT24113395
15. Ananthakrishnan, V., Kondaveeti, D., & Mohammed, A. S. (2025). GenAI-driven semantic ETL: Synthesizing self-optimizing SQL & PL/SQL. Journal of Knowledge Learning and Science Technology, 4(2), 29–43.
16. Rengarajan, A., & Rajagopalan, S. (2021). Chaos blend LFSR-duo approach on FPGA for medical image security. In Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020 (Vol. 3, p. 155).
17. Muthirevula, G. R., Kotapati, V. B. R., & Ponnoju, S. C. (2020). Contract Insightor: LLM-generated legal briefs with clause-level risk scoring. European Journal of Quantum Computing and Intelligent Agents, 4, 1–31.
18. Gurajapu, A., Anumolu, S., Garimella, V., Chundi, V. M. S. R., & Gubbala, V. S. A. P. (2025). Goal-driven autonomous agents for SLA-aware network orchestration. Frontiers in Computer Science and Artificial Intelligence, 4(1), 78–83.
19. Nallamothu, T. K. (2024). Empowering analysts with AI: Evaluating Nuance DAX Copilot in business intelligence environments. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10624–10633.
20. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive analysis of artificial intelligence applications for early detection of ovarian tumours: Current trends and future directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–9). IEEE.
21. Konda, S. K. (2024). Sustainable energy optimization through cloud-native building automation and predictive analytics integration. World Journal of Advanced Research and Reviews, 24(3), 3619–3628. https://doi.org/10.30574/wjarr.2024.24.3.3803
22. Prasanna, D., & Manishvarma, R. (2025, February). Skin cancer detection using image classification in deep learning. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–8). IEEE.
23. Sanepalli, U. R. (2024). Enterprise lakehouse architecture for customer analytics: AI and machine learning–synchronized ingestion and compute optimization. World Journal of Advanced Research and Reviews, 23(2), 2949–2959. https://doi.org/10.30574/wjarr.2024.23.2.2418
24. Gopinathan, V. R. (2024). Meta-learning–driven intrusion detection for zero-day attack adaptation in cloud-native networks. International Journal of Humanities and Information Technology, 6(1), 19–35.
25. Jothilingam, P. (2025). Towards autonomous commissioning: Integrating digital twins, artificial intelligence and smart sensors for next-generation process control systems. Certified Journal of International Research, ISSN: 3105-6393, 5(1), 1-8.
26. Mulla, F. (2024). Choosing the best architecture for mobile applications. International Journal of Research in Computer Applications and Information Technology, 7, 2350–2363. https://doi.org/10.34218/IJRCAIT_07_02_173
27. Nagarajan, C., & Madheswaran, M. (2012). Experimental verification and stability state space analysis of CLL-T series parallel resonant converter. Journal of Electrical Engineering, 63(6), 365–372.
28. Nandhini, T., Babu, M. R., Natarajan, B., Subramaniam, K., & Prasanna, D. (2024). A novel hybrid algorithm combining neural networks and genetic programming for cloud resource management. Frontiers in Health Informatics, 13(8).
29. Charumathi, M. V., & Inbavalli, M. Familiarizing the pine nut oil by fusing it into different food products. PG and Research Department of Foods & Nutrition, Marudhar Kesari Jain College for Women, Vaniyambadi.
