Sarcouncil Journal of Multidisciplinary

Abstract: The article explores the evolution of data architecture from traditional warehouses to modern lakehouse paradigms, highlighting transformative changes in enterprise information management strategies. It examines fundamental architectural components, including transactional layers, ACID compliance mechanisms, schema enforcement capabilities, and metadata management systems that collectively enable unified analytics across diverse workloads. The scalability dimensions of lakehouse architectures are analyzed through horizontal and vertical scaling approaches, computational paradigm integration, resource optimization strategies, and performance characteristics across varied data volumes. The integration of AI/ML workflows within lakehouse environments is detailed, focusing on feature engineering processes, model lifecycle management, lineage tracking mechanisms, and operational practice integration. Industry-specific implementation patterns across retail, finance, healthcare, and manufacturing sectors demonstrate how these architectures address domain-specific challenges while leveraging common foundational capabilities.