Data Modeling (DE / Analytics)

Good models reduce downstream confusion and rework.

Start with grain

Define what one row means. Many modeling bugs are really grain bugs.

Common approaches

• normalized models for core entities
• dimensional models for analytics

Practical checklist

• primary keys and uniqueness
• time fields and timezone policy
• handling late arriving data
• schema evolution and compatibility