Data Warehouse and Dimensional Modeling Questions

Design and model scalable analytical data systems using dimensional modeling principles and data warehouse architecture patterns. Core concepts include fact and dimension tables, defining and enforcing grain, surrogate keys, degenerate and role playing dimensions, conformed dimensions, and handling slowly changing dimensions including Type One, Type Two, and Type Three. Understand schema choices and trade offs such as star schema versus snowflake schema, normalization versus denormalization, and fact table types including transactional, periodic snapshot, and accumulating snapshot. Apply design decisions to meet query patterns and performance goals by considering partitioning, indexing, compression, columnar storage, and aggregation strategies. Be able to design schemas for different business domains, reason about data integration and consistency, and optimize for common analytical workloads and reporting requirements.