The exponential growth of digital data across diverse domains has necessitated the evolution of advanced data storage and analytical frameworks capable of handling high-velocity, high-volume, and high-variety datasets. Traditional data warehousing approaches, while robust for structured data and reporting, often struggle to accommodate the scale, flexibility, and real-time processing requirements imposed by modern enterprises. Emerging paradigms, including data lakes, lakehouses, and cloud-native data warehousing platforms, seek to reconcile the strengths of structured and unstructured data management, providing unified solutions for complex analytical workflows. This paper critically examines the integration of lakehouse architectures with cloud-based data warehousing systems, with a particular focus on Amazon Redshift as a representative cloud-native solution (Worlikar, Patel, & Challa, 2025). By synthesizing theoretical underpinnings, empirical implementations, and performance analyses, the study elucidates the operational, computational, and strategic implications of adopting hybrid data architectures. Key contributions include a comprehensive evaluation of ACID-compliant storage solutions such as Delta Lake, Apache Iceberg, and Hudi; the operationalization of machine learning pipelines in production contexts; and the nuanced role of metadata management in ensuring data governance and reproducibility. The findings underscore the transformative potential of integrated lakehouse and cloud data warehousing models for enterprise-scale analytics, highlighting best practices for design, deployment, and optimization while addressing critical limitations and open research questions. The paper concludes by proposing a structured framework for future adoption, emphasizing scalability, interoperability, and the alignment of technical capabilities with organizational objectives.