A guided path through relational modeling, Kimball, Data Vault 2.0, semantic layers and ontologies, ending in a capstone project. Build every exercise in the Model Builder, get instant checks, and earn a certificate for each module.
Each lesson is a few focused steps with tables, SQL and the mistakes people actually make, plus quick checks that explain every answer.
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Build it yourself
Model it on the real offdataai Model Builder canvas, define metrics on a star schema, or connect ontology concepts. No sandbox toy.
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Watch the checks go green
Every rule is checked live as you build, with a hint when you need one. Finish, then compare your work with the reference answer.
A guided path
One lesson at a time, in the right order.
Lessons unlock as you pass them, and each module opens once the one before it is complete. You learn the vocabulary first, then build source models, analytics models, a semantic layer and an ontology, and finish with a capstone that ties them together.
Your learning path: pick up where you left off.Every module has a syllabus, an outcome and a certificate.Short steps with quick checks, lesson by lesson.Build the answer on the real Model Builder canvas.Define governed metrics, checked live as you build.229 terms, each with an example and related terms.
Course catalog
From your first fact table to an ontology AI agents can use.
Projects: build it end to end
One business, start to finish. Concepts, source model, star schema, metrics, ontology and guardrails, each step building on the last.
End-to-end projectIntermediate9 lessons · about 120 minutes
Brewly roasts specialty coffee and sells it two ways: subscriptions (a bag every week or every month, on one of three plans) and one-off orders from its web shop. It has 40,000 customers and a small team that keeps asking questions nobody can answer quickly:
Every step continues your own work from the last: concepts, source model, star schema, history, snapshot, metrics, ontology and guardrails.
Outcome: You can carry a real business case from requirements to a governed, agent-ready data model and explain every decision.
1
Meet Brewly: name the business concepts
Before any table design, agree what the business is made of.
Build concepts and relationships · 12 min · instant checks
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Model Brewly's source system
Fix the order table that can only hold one coffee, and connect every table.
Build it in the Model Builder · 14 min · instant checks
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Design Brewly's star schema
Build the order-line fact and its dimensions on top of your source model.
Build it in the Model Builder · 16 min · instant checks
4
Add history and a second date
Keep revenue by city true when customers move, and measure shipping time.
Build it in the Model Builder · 12 min · instant checks
5
Model subscriptions as a monthly snapshot
MRR and churn aren't events. Capture the state of every subscription each month.
Build it in the Model Builder · 14 min · instant checks
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Define Brewly's semantic layer
Turn your star into the governed metrics every dashboard and agent will share.
Define governed metrics · 14 min · instant checks
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Map Brewly's ontology onto your model
Your step 1 concepts come back. Now they point at the analytics model.
Build concepts and relationships · 12 min · instant checks
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Make Brewly's model safe for AI agents
Move personal data where the assistant can't reach it, without losing anything.
Build it in the Model Builder · 12 min · instant checks
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Ship it: answer Brewly's questions
Put your model, metrics and ontology together and answer the team's real questions.
The vocabulary and the rules of thumb behind 3NF, Kimball, Data Vault, semantic layers and ontologies: what every term means, with examples, and how the approaches fit together.
Outcome: You can explain every core term in data modeling, semantics and ontology, and apply the best practices that go with them.
1
Relational modeling (3NF): terms and best practices
Entities, keys, relationships and the normal forms: the vocabulary of operational data models and how to apply it.
Knowledge check · 25 min · instant checks
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Dimensional modeling (Kimball): terms and best practices
Facts, dimensions, grain and slowly changing dimensions: the vocabulary of analytical models and how to apply it.
Knowledge check · 25 min · instant checks
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Data Vault 2.0: terms and best practices
Hubs, links and satellites, the specialized satellites and links, the business vault and how a vault is loaded: every Data Vault 2.0 term with an example, plus the rules that keep a vault auditable and easy to extend.
Knowledge check · 30 min · instant checks
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Semantic modeling: terms and best practices
Entities, measures, metrics and dimensions; metric types; additivity and the fan-out and chasm traps; time spines; governance; and how a semantic layer serves BI tools and AI assistants.
Knowledge check · 30 min · instant checks
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Ontology: terms and best practices
Concepts, instances, properties and relationships; taxonomies, is-a and part-of; definitions, labels and synonyms; RDF, OWL and SKOS in practice; mapping to tables; and how ontologies ground AI agents.
Knowledge check · 30 min · instant checks
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How it fits together: one architecture from source to AI agent
The capstone of Foundations: how 3NF sources, staging, Data Vault, Kimball marts, a semantic layer and an ontology-backed context layer form one stack, with the terms for moving, defining and governing data, and one question traced from source tables to an agent's answer.
Give AI agents the context to answer data questions correctly: definitions, metrics, join paths and access rules.
Outcome: You can design the context an agent needs to turn a business question into a correct, governed answer.
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What context an agent needs
A schema tells an agent where data is stored. To answer correctly it also needs definitions, metrics, join paths, rules and freshness.
Build concepts and relationships · 12 min · instant checks
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Descriptions agents can act on
An agent picks a metric by reading its description. Say the grain, what is included and what is excluded.
Define governed metrics · 12 min · instant checks
3
Synonyms and disambiguation
People say Customer, Client and Account for one thing, and Account for two different things. Write both down.
Build concepts and relationships · 12 min · instant checks
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Governed join paths
Relationships with specific verbs are the paths an agent walks to write joins. Declare them, and the agent stops inventing its own.
Build concepts and relationships · 12 min · instant checks
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Guardrails: PII and what agents must not see
Mark personal data, move it to a restricted table, and keep the tables agents query free of it.
Build it in the Model Builder · 14 min · instant checks
Certificates
Show what you can build, not just what you watched.
A certificate for every module and one for completing the whole path.
Earned by passing hands-on exercises, checked against fixed design rules: grain, keys, history, governed metrics, concept definitions.
Verifiable: each certificate has its own public page and credential ID.
One click to LinkedIn: add it to Licenses & certifications, or share it as a post with the certificate as the preview.
Sample certificate
Glossary
229 data modeling terms, explained with examples.
Every term from the Foundations module in one searchable glossary: what it means, a concrete example, the other names it goes by and the terms around it, with a link back to the lesson that teaches it.
An offdataai Explorer plan, which is $1 a month. It includes every course and every hands-on exercise, plus the Model Builder, the 174 templates and SQL for every warehouse.
How are the exercises checked?
Each exercise has a set of rules, such as the fact table's grain, the keys a Type 2 dimension needs, or whether a metric counts distinct orders. The checklist updates live as you build, and each rule you haven't met yet has a hint one click away. Checks are deterministic, so the same model always gets the same result.
Do I need to know SQL?
Basic SQL helps, but the exercises are about design decisions, not syntax. Beginner tracks start from what a row in a table means.
Who writes the lessons?
Data architects who design warehouses for a living. Every lesson is tied to a real pattern you can also open at full size in the template gallery.
In what order do I learn?
One guided path: Foundations first (the vocabulary and best practices of every approach), then relational modeling, dimensional modeling, Data Vault 2.0, a semantic layer, an ontology, the context layer for AI agents, and finally a capstone project. Lessons unlock one at a time as you pass them, and each module opens when the previous one is complete.
Do I get a certificate?
Yes. Every module earns its own certificate, and completing the whole path earns a programme certificate. Each one has a public verification page with a credential ID, and you can add it to your LinkedIn profile or share it as a post in one click. The name on it is the name on your account.
What is the capstone project?
You take one fictional business, Brewly, from its brief to an AI-ready data product: business concepts, the source model, a star schema with history, a subscription snapshot, governed metrics, an ontology and PII guardrails. Each step continues your own work from the step before, and the last one answers the team's real questions with what you built.
Is AI used to grade my work?
No. Every exercise is checked against fixed design rules, so the same work always gets the same result and nothing you do in Learn uses AI credits.
Can I cancel?
Yes, anytime, from the billing page in the app. Your progress stays on your account.
