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Abstract
Efficient logical design is critical for optimizing the performance, scalability, and maintainability of data warehouses. This paper examines techniques for structuring the logical schema to support both analytical workloads and high-performance querying. Approaches including star, snowflake, and fact constellation schemas are evaluated alongside indexing strategies, materialized views, and partitioning methods to enhance query efficiency. The study also explores query optimization techniques such as cost-based optimization, query rewriting, and aggregate navigation. Comparative analysis demonstrates how the integration of well-structured logical design with advanced query optimization strategies significantly improves response time, reduces resource consumption, and supports evolving analytical requirements in modern enterprise environments.
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