Intelligent Lakehouse Architectures For Real-Time Enterprise Decision Systems

Abstract

The lakehouse has emerged as the leading architectural pattern for enterprise data platforms over the past five years. It combines the elastic scale and openness of the data lake with the transactional reliability and SQL performance of the data warehouse. This article examines intelligent lakehouse architectures across eight themes: the evolution from data warehouses through data lakes to lakehouses; unified analytics organized around medallion data zones and open table formats; real-time streaming intelligence built on durable event logs; delta processing and incremental computation that convert expensive full rebuilds into cheap incremental merges; AI-native data infrastructure including feature stores, vector stores, and model registries; performance optimization through partitioning, clustering, and predicate pushdown; cost-efficient analytics through compute-storage decoupling; and industry-specific applications across six sectors. Two mathematical interludes derive quantitative impact claims from canonical research. Results show that incremental delta processing achieves speedups of 100 to 10,000 times over full rebuilds for typical change rates and that the combination of column projection, partition pruning, and clustering can reduce query scan volume by four orders of magnitude. Industry applications in financial services, retail, healthcare, insurance, manufacturing, and the public sector demonstrate that the lakehouse is not merely a technical pattern but a foundation for the next generation of enterprise decision systems.