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Topic

Data modeling

Data modeling shows up across 3 courses in 1 layerof the data platform stack. Here's where it's taught, a free way to practice it, and what to learn next.

Where it's taught

∿Semantic & Metrics Layer

Data Vault 2.0

The audit-first, source-volatile-first alternative to Kimball. Hubs, links, satellites, hash keys, and PIT tables — and the honest discussion of when Vault is the right tool versus when it's cosplay.

10 ch · 2h 8m

1 free

One Big Table & Wide-Table Design

The columnar-engine-native modeling style. When 200-column denormalized tables go from heresy to the right answer — and where OBT's quiet costs (update amplification, fan-out, governance) live.

8 ch · 1h 28m

1 free

Choosing a Modeling Style

The decision framework that spans Kimball, Vault, and OBT. By the end, the choice is defensible — you can name the inputs, predict the costs, and explain why one approach wins for your specific team, sources, engine, and change rate.

6 ch · 1h 4m

1 free

Related topics

↗data vault↗one big table↗denormalization↗dimensional modeling↗feature stores↗hash keys↗hubs links satellites↗insert-only loading↗kimball↗modeling tradeoffs

Start learning data modeling free

The first chapter of every course is free to read — no account needed.

Start: Data Vault 2.0 →All strata
Petascale Labs

The physics layer of data

From byte-level storage to business-grade metrics. Built with depth, not breadth.

Curriculum

Data Engineer RoadmapAll strataStorage & File FormatsIngestion & TransportOpen Table FormatsCompute EnginesOrchestration & PipelinesQuery Engines & OLAPSemantic & Metrics LayerPII & Data Governance

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All toolsParquet ViewerFreeSCD PlaygroundFreePII Masking GeneratorFree

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