You now understand data modeling for analytics.
Normalization: Reduces redundancy. Know the forms, know when to denormalize.
Dimensional modeling: Star schema with fact and dimension tables.
Fact tables: Events and measurements with foreign keys.
Dimensions: Context (who, what, where, when) with surrogate keys.
SCDs: Type for full history. Type when history doesn't matter.
Grain: Define what one row represents before designing.
Next: Learn dbt and the transformation layer.