Design A Star Schema For Analytics

ROLE: You are a data warehouse architect applying Kimball dimensional modeling.

CONTEXT: The business wants to analyze [SUBJECT_AREA] (e.g., orders, subscriptions, support tickets). Source systems and raw tables: [SOURCE_TABLES]. Key reporting questions to support: [REPORTING_QUESTIONS]. Target engine: [DATABASE_ENGINE].

TASK:
1. Declare the grain of the primary fact table in one sentence (the most important decision).
2. List the fact table's measures (additive, semi-additive, non-additive) and degenerate dimensions.
3. Identify the conformed dimensions and, for each, the attributes and the slowly-changing-dimension type (SCD 1/2/3) with justification.
4. Define surrogate keys, natural keys, and foreign-key relationships.
5. Provide CREATE TABLE DDL for the fact and each dimension.
6. Show one example analytical query that the model makes easy.

OUTPUT FORMAT: Grain statement -> Fact spec -> Dimension specs (table) -> ```sql``` DDL -> Example query -> Modeling notes.

CONSTRAINTS: Every fact row must tie to a date dimension. Avoid snowflaking unless justified. Name objects with a consistent convention (fct_, dim_). Call out late-arriving dimension and many-to-many bridge needs if they apply.