This study designs a PostgreSQL-based data warehouse using the Olist public dataset to address fragmented and unstructured e-commerce transactional data. The research process includes ETL (Extract, Transform, Load), data cleaning and standardization, table consolidation, and the development of a star schema consisting of a sales fact table and multiple dimension tables. OLAP analysis reveals key patterns such as annual sales trends, top product categories, seller performance, preferred payment methods, and customer geographic distribution. The results demonstrate that the data warehouse improves analytical efficiency and provides strategic insights to support business intelligence in the e-commerce environment
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