← Microsoft Interview Insights
I went straight to a star schema with a bookings fact table and dimensions for hosts, guests, listings, dates, and locations.
Start by clarifying the business goals and key analytical questions the warehouse must support, then propose a dimensional model with fact and dimension tables. Discuss trade-offs between star and snowflake schemas, and how to handle slowly changing dimensions and scalability.
Pro tip: Demonstrate maturity by acknowledging that the schema will evolve; propose a layered architecture (staging, integration, presentation) and mention how you would handle schema changes without disrupting downstream consumers.
Ask about the primary use cases (e.g., booking trends, host performance, pricing analysis) and the expected query patterns to determine granularity and dimensions.
List core business events (e.g., bookings, payments, reviews) as fact tables and their associated descriptive attributes (e.g., user, listing, date, location) as dimension tables.
Decide between star and snowflake schema based on query performance and storage; justify your choice with trade-offs like simplicity vs. normalization.
Explain how to track historical changes in dimensions (e.g., Type 2 SCD for host attributes) and the impact on fact tables.
Discuss partitioning, indexing, and aggregation strategies to handle large data volumes and ensure fast query performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.