A star schema is a warehouse modeling style with a central fact table of measurable events and surrounding dimension tables that describe those events (who, what, where, when).
dim_customer
\
dim_date --- fct_orders --- dim_product
/
dim_storeFacts
- Many rows, numeric measures (
amount,quantity) - Foreign keys to dimensions
- A clear grain: "one row per order" or "one row per order line"
Dimensions
- Descriptive attributes (
customer_name,product_category,region) - Used for filters and group-bys in BI
Why interviewers like it
Stars are simple for BI tools, performant for aggregates, and force you to clarify grain. Confusing order-level and line-item-level facts is a classic modeling bug.
Tiny example
fct_order_items holds qty and amount plus keys to dim_date, dim_customer, dim_product. Revenue by category by day is a join + SUM(amount) GROUP BY category, date.
Interview tip
Always state the fact grain first. Everything else in a star hangs from that sentence.