Designing a warehouse model for feature adoption
WHAT IT TESTS: Dimensional modeling skill. OUTLINE: Star schema with a feature-usage fact table at a defined grain, surrounded by user, feature, date, and device dimensions. RED FLAG: One giant wide table or modeling without defining the grain.
WHAT IT TESTS: Whether you can design an efficient, queryable analytics schema. ANSWER OUTLINE: Apply dimensional modeling with a star schema. Define the grain first, for example one row per feature-usage event. Build a fact table of foreign keys and additive measures, surrounded by conformed dimensions: dim_user, dim_feature, dim_date, dim_device. Use slowly changing dimensions for attributes like plan. Adoption metrics become joins over the fact. RED FLAG: Proposing one giant table, skipping grain, or a deep snowflake that slows queries.
Read the original → interview
- #dimensional-modeling
- #star-schema
- #data-warehouse
- #analytics
- #etl
Get five bites like this every day.
Tezvyn delivers a daily feed of 60-second tech bites with quizzes to lock in what you learn.