9 interactive modules covering star schemas, SCDs, Data Vault, modern patterns, lakehouse modeling — with quizzes, visual diagrams, and scenario-based practice.
From fundamentals to brain-teasers — structured for interview success
Why data modeling matters, conceptual vs logical vs physical models, OLTP vs OLAP, normalization trade-offs, and where modeling fits in the modern data stack.
Grain as the foundation, transactional vs snapshot fact tables, factless facts, degenerate dimensions, and additive vs semi-additive measures.
Star schema benefits, snowflake trade-offs, conformed dimensions, Data Vault basics, and when to pick each approach for different workloads.
SCD Types 0 through 6 with examples, implementing SCD2 step-by-step, surrogate vs natural keys, late-arriving dimensions, and mini-dimensions.
Bridge tables, role-playing dimensions, junk dimensions, outrigger dimensions, preventing double-counting, and model validation strategies.
15 tricky scenario questions, "design this model" exercises, common pitfalls, rapid-fire Q&A, and the gotchas that trip up 90% of candidates.
Hubs, Links, Satellites, hash keys, PIT tables, bridge tables in Data Vault, and the decision framework for Data Vault vs Kimball.
One Big Table (OBT), Activity Schema, wide tables in columnar storage, EAV pattern, and modeling for ML feature stores with point-in-time correctness.
Medallion architecture (Bronze→Silver→Gold), schema-on-read vs write, partitioning strategies, storage format integration, and real-world lakehouse problems.