Data Modeling: Learning Path
| # | Module | You will be able to |
|---|---|---|
| 1 | The modeling process | Run a modeling interview: questions → grain → dimensions → facts → validation |
| 2 | Dimensional modeling deep dive | Pick fact types, handle additivity, bridges, junk/mini/role-playing dimensions |
| 3 | Slowly changing dimensions | Implement SCD 0–7, SCD2 MERGE, dbt snapshots, point-in-time joins |
| 4 | Modern modeling | Choose between Kimball, Data Vault, OBT, activity schema; model events and streams |
| - | Cheatsheet | Revise quickly |
Then practise with the 10 case studies.