Organizations face challenges in structuring data for analytics, as traditional Online Transaction Processing (OLTP) systems prioritize speed and reliability rather than analytical efficiency. OLTP systems, which use a Third Normal Form schema, are not optimal for complex queries needed in Online Analytics Processing (OLAP) systems. Ralph Kimball's dimensional modeling, including the Star Schema, offers a solution by transforming OLTP data into a more analyzable format through ETL processes, though this can be time-consuming and require expertise. Despite the benefits of improved query performance and ease of use, adopting star schemas involves data duplication and maintenance efforts. Cube's universal semantic layer presents a modern alternative, enabling logical data mapping without extensive ETL, thereby providing quick and scalable data access across OLTP and OLAP systems. This approach facilitates both immediate data needs and long-term data strategy, allowing organizations to maintain agility and optimize their analytics capabilities in a cloud-native environment.