Last Updated Sep 2025Microsoft Fabric is a unified platform that can meet your organization's data and analytics needs. Data engineering in Microsoft Fabric provides a Spark platform with great authoring experiences. Attend this course to learn about designing, building, and maintaining infrastructures and systems with Data engineering in Microsoft Fabric to enable your organizations to collect, store, process, and analyze large volumes of data.
Duration - 12 Hours
Level - Advanced
Style - Self paced
Target Audience - Project Ready with Labs
Certification - No
Hands on Labs - Yes
Solution Areas - Azure - Cloud & AI Platform, Unify Your Data Platform
In this module, you will learn about Overview of Microsoft Fabric, Microsoft Fabric Terminology, Self-help with the Fabric contextual Help pane, searching for content, Microsoft Fabric settings, Working with Workspaces, discovering data in OneLake and data hub, Promoting or certifying items, applying sensitivity labels and Administering Microsoft Fabric.
In this module, you will learn about Overview of Data Engineering in Microsoft Fabric, Lakehouse Overview, Ways to create a Lakehouse in Microsoft Fabric, getting data into Fabric Lakehouse, Data Factory in Microsoft Fabric, Working with Data Pipelines in Microsoft Fabric, Ingesting Data with Dataflows Gen2, Workspace roles, permissions and sharing and Introduction to Spark compute in Microsoft Fabric.
In this module, you will learn about Apache Spark job definition, Spark Administration settings, Apache Spark monitoring in Microsoft Fabric, Lakehouse and Delta Lake tables, how to use Microsoft Fabric notebooks, Synapse Visual Studio Code extension, Decision Guide for copy activity, dataflow or Spark, Lakehouse end-to-end scenario and architecture.
In this module, you will learn about Overview of Real-time Analytics in Microsoft Fabric, Event Streams in Microsoft Fabric, Creating a KQL Database, Querying data in a KQL Queryset, Introduction to Data Science in Microsoft Fabric, Working with Notebooks in Microsoft Fabric, Preparing Data, Training machine learning models, Tracking models, experiments and Autologging and Model Scoring
Take this assessment to validate your skills gathered from the self-paced online learning course completed in this course to mark your completion.
Share your feedback with us regarding your experience!
Intermediate
Beginner
Intermediate
Beginner
No. of
Learners - 139