Lakehouse Modernization for Retail Analytics Performance

This document is a case study detailing the lakehouse modernization project undertaken by a large U.S. based omnichannel retail enterprise. The objective of the project was to enhance the analytics foundation to support unified insights across sales, inventory, and customer domains. The existing data pipelines were characterized by a lack of structure and consistency, which limited query performance and hindered timely business decisions, especially during peak seasonal demand periods. TechWish implemented a structured lakehouse architecture on Databricks, utilizing the Medallion Architecture to standardize data quality and optimize performance. The solution included scalable ETL pipelines and automated ingestion frameworks, which collectively improved query performance and reduced data processing latency. The results indicated a stabilization of retail analytics performance during peak periods, enabling faster access to insights and increasing trust in reporting. The organization also achieved a reduction in operational overhead through repeatable pipeline frameworks, establishing a scalable foundation for future analytics initiatives.