Data Modeling

This section teaches how to design data models that are correct, maintainable, and fast enough for analytics.

It is designed for:

• learners who need clear mental models
• advanced data engineers who need production-grade tradeoff frameworks

• OLTP vs OLAP and why workload shape matters
• grain contracts and fact table patterns
• SCD Type 1/2/3 and dimension history management
• bridge table modeling for many-to-many relationships
• date/time semantics and fiscal calendar modeling
• model-level data quality and reconciliation strategy
• serving-layer and metric-layer operating models
• incremental processing, backfills, and restatement controls
• architecture tradeoffs: star schema vs data vault vs 3NF
• end-to-end datamart implementation patterns

1. Foundations: OLTP vs OLAP, time semantics, and model QA
2. Dimensional Modeling: star schema, fact patterns, SCD, and bridges
3. Data Vault: hubs, links, satellites, and vault-to-mart transition
4. Serving Layer: serving assets, metric layer, and incremental strategy
5. Tradeoffs: architecture decisions plus end-to-end case study
6. Capstone: Bronze, Silver, and Gold graded assignments

For each lesson:

• start with the core concept
• apply the checklist in your own domain
• identify one design risk and one mitigation
• decide what to optimize: model simplicity, load flexibility, or query speed

Run this quick query to reinforce model-thinking at a usable analytical grain.

• OLTP vs OLAP and Grain
• Date and Time Modeling
• Star Schema Basics