Get started with Microsoft Fabric
At a glance
-
Level
-
Skill
-
Product
Explore how to implement data analytics solutions on a single platform with Microsoft Fabric. Integrate, transform, and store data to train AI models and create insightful reports.
Prerequisites
You should be familiar with basic data concepts and terminology, including data engineering, modeling, and analysis.
Achievement Code
Would you like to request an achievement code?
Modules in this learning path
Discover how Microsoft Fabric can meet your enterprise's analytics needs in one platform. Learn about Microsoft Fabric, how it works, and identify how you can use it for your analytics needs.
Lakehouses in Microsoft Fabric combine data lake storage flexibility with data warehouse analytical capabilities. Learn how to create a lakehouse, ingest and transform data, and query data with SQL and Spark.
Understand what a Fabric data warehouse is, why it provides full T-SQL transactional capabilities, and how to create, query, and transform data for analytics.
Real-Time Intelligence in Microsoft Fabric helps you ingest, process, store, visualize, and act on data in motion to get insights from events as they happen.
In Microsoft Fabric, data scientists can manage data, notebooks, experiments, and models while easily accessing data from across the organization and collaborating with their fellow data professionals.
You'll explore how to use the capabilities of SQL databases within the Microsoft Fabric environment, which provides a unified platform for managing and analyzing data.
Design semantic models for scale in Microsoft Fabric. Choose the right storage mode, design star schema relationships for clarity and performance, create scalable calculation patterns, and configure settings that support large datasets and concurrent consumption.
Microsoft Fabric IQ is part of Microsoft IQ, an enterprise intelligence layer that elevates business data into shared concepts usable by people and AI agents. Learn about the six items in the IQ workload—ontology items, data agents, Graph in Microsoft Fabric, Power BI semantic models, operations agents, and planning—and discover how ontology modeling starts with business concepts rather than specific use cases.