Managing Data Warehouses (DWs) presents increasing challenges, particularly in handling diverse query loads that affect overall performance. This paper aims to enhance the performance of overloaded data warehouses by improving key factors such as storage efficiency, retrieval speed, and query execution time. To optimize these areas, strategies such as indexing, data compression, materialized views, and partitioning are applied. Star Schema designs are employed in Data Modeling Optimization to improve performance in read-heavy environments. Columnstore indexes are used to expedite data retrieval, while Row-level and Page-level compression reduce storage requirements and improve I/O performance. Materialized Views precompute complex queries to eliminate processing overhead, and partitioning increases retrieval efficiency by dividing large tables based on key attributes. Using the Olympic Games Data Warehouse as a case study, the implementation of these techniques in Microsoft SQL Server 2022 demonstrates substantial improvements in storage performance, scalability, and query efficiency. These enhancements contribute to maximizing the utility of the data warehouse, improving data accessibility, reducing operational costs, and supporting continuous performance improvements through ongoing monitoring and optimization.