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

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.