Designing Semantics with Microsoft Fabric IQ

Design meaning once, and reuse it everywhere, by using semantic models in Fabric IQ !

Course Summary

Do you want to get working with semantic models in Microsoft Fabric IQ ?

As Fabric IQ is in preview, the focus of this course is on semantic design decisions that remain valid, even as the tools, interfaces, and features evolve. It does not aim to walk through every UI option or provide beginner-level Fabric training.

It’s about designing meaning once, and reusing it everywhere, by using semantic models in Fabric IQ. It assumes you already understand the basics of Fabric and analytics tooling.

Are you responsible for shared understanding, not just individual outputs?
Do you need to design metrics that will be reused across teams, reports, and AI workloads?
Have you ever had to explain why two dashboards show different numbers for the same question?

If that matches the kind of responsibility you have, you’re in the right place. You’ll get clear explanations, hands-on exercises, and short quizzes to make sure you not only understand the concepts - but can confidently apply them.

And while they’re obviously optional, we encourage you to complete the practical exercises. We have tried to make this as easy as possible. The main thing you’ll need is a trial (or real) Fabric account.

Enroll now for $195 USD !

Modules and Lessons

Module 0: Getting started

Who is this course for ?
Who is Greg ?
What will I learn in this course ?
Configuring your lab environment

Module 1: What Fabric IQ is (and is not)

Module introduction
Why semantic chaos exists
What Fabric IQ introduces
What Fabric IQ is not
Fabric IQ preview features
Lab core business scenario
Lab source dataset walkthrough
Lab 1
Quiz 1

Module 2: Semantic foundations and mental models

Module introduction
Entities, attributes, metrics and meaning
Meaning layer vs data layer
Semantics for humans and machines
Demo - Semantic mapping
Bad semantic model example
Better semantic model
Why this difference matters
Semantic design rules that scale
Lab shared enterprise entities walkthrough
Lab 2
Quiz 2

Module 3: Designing business metrics with Fabric IQ

Module introduction
Enterprise-safe metrics
Centralized vs local calculations
Metric versioning and change impact
Demo - Metric lifecycle
Metric to entity relationship mapping
Lab metrics walkthrough
Common mapping mistakes
Design rule of thumb
Built-in semantic problems
Demo - Metrics walkthrough
Lab 3
Quiz 3

Module 4: Ontologies, relationships, and meaning at scale

Module introduction
What is an ontology ? When to introduce ontologies
Cross-domain semantics
Demo - Cross domain ontologies
Lab 4
Quiz 4

Module 5: Fabric IQ with Power BI, Copilot, and AI workloads

Module introduction
Fabric IQ and Power BI
Natural language queries
Semantics and AI trust
Demo - AI query before semantics
Demo - AI query after semantics
Lab 5
Quiz 5

Module 6: Governance, ownership, and lifecycle management

Module introduction
Semantic ownership
Change control without bureaucracy
Discoverability and audit
Demo - Governance metadata
Lab 6
Quiz 6

Module 7: Common mistakes and anti-patterns

Module introduction
Everything is a Metric Anti-Pattern
Ontology over-engineering
Recreating Power BI models in Fabric IQ
Letting AI drive semantic design
Example mapping issues - Revenue
Example mapping issues - Order Count
Example mapping issues - Average Order Value
Example mapping issues - Gross Margin
Why poor mappings often look correct
Design lessons from the comparisons
Lab 7
Quiz 7

Module 8: Reference architecture and end-to-end walkthrough

Module introduction
Recommended reference architecture
End-to-end walkthrough
When not to use Fabric IQ
Demo - End-to-end Recap
Lab 8
Quiz 8

Module 9: Next steps

Summary and further steps

Enroll now for $195 USD !