Abstract

The rapid digital transformation of enterprise web applications has intensified the demand for scalable, intelligent, and secure data analytics platforms capable of handling heterogeneous workloads. Cloud lakehouse architectures have emerged as a unified solution that integrates the flexibility of data lakes with the reliability and performance of data warehouses. This paper presents a comprehensive study of secure AI-powered cloud lakehouse platforms designed to support SAP financial analytics and healthcare image processing within broadband-connected enterprise web applications. SAP-based financial systems generate large volumes of structured transactional data that require real-time analytics, regulatory compliance, and strong security guarantees. In parallel, healthcare systems increasingly rely on cloud-based image processing and artificial intelligence to support diagnostic accuracy and clinical decision-making. By embedding AI-driven analytics, automation, and security intelligence into a cloud lakehouse framework, organizations can achieve efficient data management, advanced analytics, and continuous threat detection. The paper explores architectural design principles, data processing workflows, AI integration strategies, and security mechanisms that enable unified analytics across financial and healthcare domains. Furthermore, the role of high-speed broadband connectivity in enabling low-latency data access and distributed enterprise web applications is examined. The proposed methodology provides a structured approach for designing, implementing, and evaluating such platforms, contributing to secure and intelligent enterprise cloud ecosystems.