AI-Enabled Cloud Lakehouse for Large-Scale Data Warehousing: SAP Integration for Secure Analytics and Tableau-Driven Reporting

Nathaniel Liam Carrington

doi:10.15662/IJRAI.2025.0805013

Authors

Nathaniel Liam Carrington Senior IT Manager, Australia Author

DOI:

https://doi.org/10.15662/IJRAI.2025.0805013

Keywords:

AI-enabled analytics, cloud lakehouse, large-scale data warehousing, SAP integration, secure analytics, fraud detection, Tableau reporting, enterprise data platforms

Abstract

The rapid growth of enterprise data across transactional systems, IoT platforms, cybersecurity logs, and analytical applications has intensified the need for scalable, secure, and intelligent data warehousing architectures. Traditional data warehouses struggle to accommodate heterogeneous data formats, real-time analytics, and advanced artificial intelligence (AI) workloads. This paper presents an AI-enabled cloud lakehouse framework for large-scale data warehousing that integrates SAP enterprise systems with advanced security analytics and Tableau-driven reporting. The proposed architecture combines the flexibility of data lakes with the governance and performance of data warehouses, enabling unified analytics across structured, semi-structured, and unstructured data. AI and machine learning models are embedded within the lakehouse to support secure analytics, fraud detection, and anomaly identification, while SAP integration ensures consistency with enterprise transactional data. Tableau is employed as a visualization layer to provide dynamic, interactive, and role-based reporting for decision-makers. Experimental analysis and enterprise use-case evaluations demonstrate that the proposed framework improves data accessibility, analytics performance, security visibility, and decision accuracy compared to conventional warehouse-centric approaches. The framework is particularly suitable for data-intensive enterprises seeking secure, scalable, and insight-driven analytics in cloud environments.

References

1. Inmon, W. H. (2005). Building the Data Warehouse. Wiley.

2. Kimball, R., & Ross, M. (2002). The Data Warehouse Toolkit. Wiley.

3. Armbrust, M., et al. (2018). Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores.

4. Okpara, K. (2025). Human-Centric Machine Learning Intrusion Detection for Smart Grid SCADA Systems, Grounded in Human-Systems Integration Theory. Available at SSRN 5295278.

5. Pimpale, S. (2025). Synergistic Development of Cybersecurity and Functional Safety for Smart Electric Vehicles. arXiv preprint arXiv:2511.07713.

6. Kesavan, E. (2023). ML-Based Detection of Credit Card Fraud Using Synthetic Minority Oversampling. International Journal of Innovations in Science, Engineering And Management, 55-62.

7. Kasireddy, J. R. (2023). Optimizing multi-TB market data workloads: Advanced partitioning and skew mitigation strategies for Hive and Spark on EMR. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 6982–6990. https://doi.org/10.15680/IJCTECE.2023.0603005

8. Thumala, S. R., Mane, V., Patil, T., Tambe, P., & Inamdar, C. (2025, June). Full Stack Video Conferencing App using TypeScript and NextJS. In 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 1285-1291). IEEE.

9. Kusumba, S. (2025). Driving US Enterprise Agility: Unifying Finance, HR, and CRM with an Integrated Analytics Data Warehouse. IPHO-Journal of Advance Research in Science And Engineering, 3(11), 56-63.

10. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.

11. Parameshwarappa, N. (2025). Building Bridges: The Architecture of Digital Inclusion in Public Services. Journal Of Multidisciplinary, 5(8), 96-103.

12. Vasugi, T. (2023). Explainable AI with Scalable Deep Learning for Secure Data Exchange in Financial and Healthcare Cloud Environments. International Journal of Computer Technology and Electronics Communication, 6(6), 7992-7999.

13. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.

14. Ramakrishna, S. (2023). Cloud-Native AI Platform for Real-Time Resource Optimization in Governance-Driven Project and Network Operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6282-6291.

15. Nagarajan, G. (2024). A Cybersecurity-First Deep Learning Architecture for Healthcare Cost Optimization and Real-Time Predictive Analytics in SAP-Based Digital Banking Systems. International Journal of Humanities and Information Technology, 6(01), 36-43.

16. Navandar, P. (2022). The Evolution from Physical Protection to Cyber Defense. International Journal of Computer Technology and Electronics Communication, 5(5), 5730-5752.

17. Cherukuri, B. R. (2025). Enhanced trimodal emotion recognition using multibranch fusion attention with epistemic neural networks and Fire Hawk optimization. Journal of Machine and Computer, 58, Article 202505005. https://doi.org/10.53759/7669/jmc202505005

18. Potdar, A., Kodela, V., Srinivasagopalan, L. N., Khan, I., Chandramohan, S., & Gottipalli, D. (2025, July). Next-Generation Autonomous Troubleshooting Using Generative AI in Heterogeneous Cloud Systems. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-7). IEEE.

19. Genne, S. (2025). Engineering Secure Financial Portals: A Case Study in Credit Line Increase Process Digitization. Journal Of Multidisciplinary, 5(7), 563-570.

20. Kumar, S. S. (2024). Cybersecure Cloud AI Banking Platform for Financial Forecasting and Analytics in Healthcare Systems. International Journal of Humanities and Information Technology, 6(04), 54-59.

21. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.

22. 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.

23. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.

24. D. Johnson, L. Ramamoorthy, J. Williams, S. Mohamed Shaffi, X. Yu, A. Eberhard, S. Vengathattil, and O. Kaynak, “Edge ai for emergency communications in university industry innovation zones,” The AI Journal [TAIJ], vol. 3, no. 2, Apr. 2022.

25. Chivukula, V. (2024). The Role of Adstock and Saturation Curves in Marketing Mix Models: Implications for Accuracy and Decision-Making.. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(2), 10002–10007.

26. Singh, A. (2023). Self-evolving IoT systems through edge-based autonomous learning. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7547–7555. https://doi.org/10.15662/IJEETR.2023.0506011

27. Kumar, R., Panda, M. R., & Sardana, A. (2025). Reinforcement Learning for Autonomous Data Pipeline Optimization in Cloud-Native Architectures. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 4(3), 97-102.

28. Kubam, C. S. (2026). Agentic AI Microservice Framework for Deepfake and Document Fraud Detection in KYC Pipelines. arXiv preprint arXiv:2601.06241.

29. Natta, P. K. (2023). Harmonizing enterprise architecture and automation: A systemic integration blueprint. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9746–9759. https://doi.org/10.15662/IJRPETM.2023.0606016

30. Abadi, D. J. (2009). Data Management in the Cloud.

31. Madabathula, L. (2022). Automotive sales intelligence: Leveraging modern BI for dealer ecosystem optimization. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 80–93. https://www.ijhit.info

32. Tableau Software. (2020). Visual Analytics and Business Intelligence.

33. Karnam, A. (2024). Engineering Trust at Scale: How Proactive Governance and Operational Health Reviews Achieved Zero Service Credits for Mission-Critical SAP Customers. International Journal of Humanities and Information Technology, 6(4), 60–67. https://doi.org/10.21590/ijhit.06.04.11

34. Ramalingam, S., Mittal, S., Karunakaran, S., Shah, J., Priya, B., & Roy, A. (2025, May). Integrating Tableau for Dynamic Reporting in Large-Scale Data Warehousing. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 664-669). IEEE.

35. Zikopoulos, P., et al. (2012). Understanding Big Data. McGraw-Hill.