I jumped straight into fact and dimension tables without really scoping the problem first.
Start by clarifying the business questions the warehouse must answer (e.g., user engagement, artist royalties, content trends), then propose a dimensional model with a central fact table (listens) and conformed dimensions (user, track, artist, time, device). Discuss trade-offs between star vs. snowflake schemas, partitioning, and slowly changing dimensions, and explain how the design supports scalability and query performance.
Pro tip: Emphasize that the schema should be optimized for the most frequent and expensive queries, and mention that you would validate the design with real query patterns and iterate based on performance metrics.
Ask about the key business questions, data volume, query patterns, and latency requirements to ensure the schema aligns with actual needs.
Determine the central fact table (e.g., listens) and the surrounding dimensions (user, track, artist, album, time, device, location) that provide context.
Decide between star and snowflake schemas, and define the grain of the fact table (e.g., one row per listen event) to balance query performance and storage.
Explain how to handle changes in dimension attributes (e.g., user subscription tier) using SCD Type 2, and how to partition fact tables by date for efficient querying.
Compare design choices (e.g., normalization vs. denormalization, partitioning strategies) and explain how the schema scales with data growth and supports fast aggregations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.