Designing a warehouse model for feature adoption

WHAT THIS TESTS Whether you understand dimensional modeling and can translate a business question, feature adoption, into a performant, queryable schema.

A GOOD ANSWER COVERS Start by declaring the grain, the most important modeling decision: one row per feature-interaction event, capturing who used which feature, when, and on what device. Build a star schema. The central fact table, fact_feature_usage, stores foreign keys to dimensions plus measures and degenerate attributes like session id. Surround it with conformed dimension tables: dim_user with attributes such as signup date, plan, and cohort; dim_feature with feature name, category, and release date; dim_date with calendar attributes for easy period rollups; and dim_device with platform, OS, and app version. Keep dimensions denormalized enough to avoid excessive joins, favoring a star over a snowflake for analytic speed. Use surrogate keys and handle slowly changing dimensions so historical attributes like a user's plan at event time are preserved with type 2 tracking.

HOW METRICS FALL OUT Adoption KPIs such as percent of active users who used a feature, or week-over-week adoption, become straightforward aggregations of the fact joined to dimensions, often materialized into a summary or aggregate table for dashboard latency.

COMMON WRONG ANSWERS One enormous wide table that is impossible to maintain and bloats storage. Skipping grain, which makes counts ambiguous. Over-normalizing into a deep snowflake that requires many joins and slows the dashboard. Storing metrics without the dimensions needed to slice them.

LIKELY FOLLOW-UPS How do you handle late-arriving events? Type 1 versus type 2 SCD trade-offs? When do you pre-aggregate?

ONE CONCRETE EXAMPLE To show adoption of a new export feature by plan, you join fact_feature_usage to dim_feature filtered to export, to dim_user for plan, and to dim_date for the week, then compute distinct users per plan over distinct active users, all from a clean star that the BI tool queries in seconds rather than scanning a sprawling raw events table.