From Traditional Data Warehouses to Lakehouse Architectures: Tackling Modern Data Challenges

Abstract

Modern data architectures are undergoing a fundamental transformation as organizations grapple with increasingly diverse and complex analytical workloads. This article examines the evolution from traditional data warehouses to lakehouse architectures, presenting a comprehensive analysis of how this hybrid approach addresses contemporary data challenges. This article explores the core principles of lakehouse design, focusing on key technologies like Delta Lake and Apache Iceberg that enable ACID compliance and schema evolution in distributed environments. Through detailed case studies across e-commerce, financial services, and supply chain sectors, this article demonstrates how lakehouse architectures effectively support both traditional business intelligence and emerging use cases such as real-time analytics and machine learning. This article also provides a systematic framework for organizations planning their migration journey, including architectural patterns, optimization strategies, and governance considerations. This article suggests that lakehouse architectures significantly enhance data teams' ability to handle diverse workloads while maintaining data reliability and performance at scale.