Data lakehouse implementation: A journey from traditional data warehouses
Year: 2025
Type: article
Abstract: This comprehensive article explores the evolution and implementation of modern data lakehouse architectures, which combine the best elements of data lakes and data warehouses to address contemporary data challenges. The article draws on extensive case studies and empirical evidence across multiple industries to document the transformation from traditional data storage systems to more flexible, scalable lakehouse solutions. Through detailed analysis of real-world implementations, the article examines critical technical challenges in schema evolution management, data quality at scale, and metadata management, presenting innovative solutions developed by successful organizations. The article presents strategies for optimizing performance and costs through intelligent storage management, query optimization, and resource governance. The article highlights approaches to data discovery that enhance accessibility across skill levels, explores workload management patterns that ensure reliable performance for diverse processing requirements, and examines governance frameworks that balance compliance with usability. Throughout the analysis, quantifiable results demonstrate the substantial business value delivered by well-implemented lakehouse architectures, including improved operational efficiency, enhanced analytical capabilities, and significant cost savings.
Authors: Srinivasa Sunil Chippada, Shekhar Agrawal, Rahul Vats
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409246562