The Evolution from Data Warehouses to Data Lakehouses: A Technical Perspective

Abstract

The traditional data warehouse has evolved substantially over the past decade as organizations face challenges with expanding data volumes and diverse data types. This evolution led to the emergence of data lakes to address scalability and flexibility limitations, followed by the development of data lakehouses as a technical convergence of both paradigms. The data lakehouse architecture implements data management features directly on cloud storage through open table formats, robust metadata management, advanced query optimization, and multi-engine support. Various implementation patterns have emerged, including cloud-native offerings from major providers, integrated vendor platforms, and customized open-source solutions. The lakehouse paradigm offers significant advantages in cost structure, performance capabilities, and governance features while maintaining the flexibility needed for modern analytical workloads.