Effective data modeling serves as the blueprint for robust analytical systems. This course focuses on designing schemas that optimize query performance, data aggregation, and reporting efficiency. We examine the structural differences between operational databases and analytical data warehouses, moving from normalization to dimensional modeling.
You will learn to architect Star and Snowflake schemas, handle historical data changes through Slowly Changing Dimensions (SCD), and implement advanced fact table patterns. The curriculum also addresses physical design considerations for modern cloud data warehouses, including partitioning, clustering, and the usage of nested data types. This material provides the technical foundation necessary for building scalable data platforms.
Prerequisites Intermediate SQL knowledge
Level:
Dimensional Modeling
Design efficient Star and Snowflake schemas optimized for analytical queries.
Fact and Dimension Patterns
Implement various fact table types and handle changing dimension data over time.
Storage Optimization
Apply partitioning, clustering, and denormalization strategies to improve performance.
Schema Architecture
Transition raw operational data into structured analytical models suitable for BI and reporting.
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