Data warehouses organize information to support business intelligence and analytics. Unlike transactional databases optimized for updates, data warehouses prioritize read performance and analytical queries. Dimensional modeling using star and snowflake schemas provides intuitive structures that analysts understand while enabling efficient query execution.
Star Schema Fundamentals
Star schemas organize data around central fact tables containing metrics and foreign keys to dimension tables with descriptive attributes. This structure mirrors how business users think about data—analyzing measures across various dimensions like time, geography, and product. Query optimization benefits from predictable join patterns and denormalized dimensions.
- Design fact tables containing numerical measures at appropriate grain levels
- Create dimension tables with descriptive attributes supporting diverse analysis needs
- Use surrogate keys for dimension tables enabling slowly changing dimension handling
- Implement date dimensions with pre-calculated attributes like fiscal periods and holidays
- Denormalize dimensions appropriately balancing query performance against maintenance
Slowly Changing Dimensions
Dimension attributes change over time—customer addresses, product categories, organizational structures. Slowly Changing Dimension (SCD) techniques track these changes appropriately. Type 1 overwrites old values. Type 2 maintains history through versioned rows. Type 3 stores limited history in additional columns. Choosing appropriate SCD types balances historical accuracy with complexity.
Performance Optimization
Data warehouse performance requires strategic optimization. Partitioning large fact tables by date enables efficient queries on recent data. Materialized views pre-compute common aggregations. Column-oriented storage reduces I/O for analytical queries. Index strategy focuses on dimension keys and common filter columns. These optimizations enable interactive query performance on large datasets.