course
Microsoft Fabric Analytics Engineer (DP-600)
Design, transform, and serve analysis-ready data at scale
Description
This course covers how to prepare, enrich, and serve data for analysis by consumers such as data analysts, report developers, and AI agents. The course focuses on designing dimensional models and transforming data by using dataflows, notebooks, and T-SQL across lakehouses, warehouses, and eventhouses in Microsoft Fabric. The course also covers building and optimizing semantic models, managing the analytics development lifecycle, and enforcing security and governance across data assets.
Audience Profile
This course is intended for data professionals with experience in data modeling, transformation, and analytics. Learners should have prior experience translating business requirements into analytical measures by using Structured Query Language (SQL) or Data Analysis Expressions (DAX). Experience building semantic models and reports in Power BI is recommended. Familiarity with Kusto Query Language (KQL) and Python is also helpful but not required.
Certification
This course also helps you prepare for the Associate exam DP-600 (Microsoft Certified: Fabric Analytics Engineer Associate)
Learning Goals
 Identify the key characteristics of OneLake, lakehouses, warehouses, and eventhouses in Microsoft Fabric. | Remember  |
| Explain end-to-end analytics in Microsoft Fabric, from data ingestion to reporting and analysis consumption. | Understand  |
| Explain how Fabric data stores differ in workload type, latency, data structure, and query patterns. | Understand |
| Apply dimensional modeling principles to design a practical star schema for analytics workloads. | Apply  |
| Implement data transformations with Dataflows Gen2, notebooks, and T-SQL to deliver consistent, analysis-ready data. | Apply |
| Demonstrate DAX calculations, relationships, and foundational measure logic in a semantic model. | Apply |
| Explain how to improve semantic model scalability and performance using targeted optimization techniques. | Understand |
| Describe the metadata and semantic context required to make analytics data AI-ready for Fabric IQ. | Understand |
| Use a controlled semantic model development lifecycle focused on change management and release practices. | Apply |
| Describe security and governance controls across workspace, data store, and semantic model layers. | Understand |
Prior Knowledge
Experience with:
- Microsoft Power BI Power BI Data Analyst
- Querying T-SQL Core Querying Data with T-SQL
- Basic Python Essentials of Python Development
Subjects
- Explore analytics data stores in Microsoft Fabric
- Design and transform analytics data in Microsoft Fabric
- Design and manage semantic models in Microsoft Fabric
- Prepare AI-ready analytics data in Microsoft Fabric
- Secure and govern analytics data in Microsoft Fabric
1: Explore analytics data stores in Microsoft Fabric
Understand how Microsoft Fabric unifies analytics storage through OneLake, and evaluate when to use lakehouses, warehouses, or eventhouses for your data workloads.
Lessons
- Introduction to end-to-end analytics using Microsoft Fabric
- Discover and connect to data in OneLake
- Get started with lakehouses in Microsoft Fabric
- Get started with data warehouses in Microsoft Fabric
- Get started with Real-Time Intelligence in Microsoft Fabric
2: Design and transform analytics data in Microsoft Fabric
Design dimensional models and apply transformations using dataflows, Spark notebooks, and T-SQL to produce consistent, analysis-ready data in Microsoft Fabric.
Lessons
- Choose data stores in Microsoft Fabric
- Design dimensional models for analytics in Microsoft Fabric
- Transform data using Dataflows Gen2 in Microsoft Fabric
- Transform data using notebooks in Microsoft Fabric
- Transform data using T-SQL in Microsoft Fabric
3: Design and manage semantic models in Microsoft Fabric
Create semantic models that serve reliable, governed analytics at scale — from initial DAX logic through performance optimization, data access control, and automated lifecycle management.
Lessons
- Create DAX calculations in semantic models
- Design semantic models for scale in Microsoft Fabric
- Optimize semantic model performance
- Enforce semantic model security
- Manage the semantic model development lifecycle
4: Prepare AI-ready analytics data in Microsoft Fabric
Prepare your semantic layer for AI by adding metadata and linguistic context to gold-layer data stores and semantic models, then generate ontologies that AI agents use to answer business questions.
Lessons
- Prepare the semantic layer for AI in Microsoft Fabric
- Understand Microsoft Fabric IQ fundamentals
- Create an ontology with Fabric IQ
5: Secure and govern analytics data in Microsoft Fabric
Implement layered access controls across Microsoft Fabric workspaces and data stores, then establish governance policies that help consumers identify trustworthy, certified analytics assets.
Lessons
- Secure data access in Microsoft Fabric
- Secure a Microsoft Fabric data warehouse
- Govern analytics data in Microsoft Fabric
Schedule
All courses can also be conducted within your organization as customized or incompany training.
Our training advisors are happy to help you provide personal advice or find Incompany training within your organization.
Trainers
Prior knowledge courses
Microsoft Power BI Data Analyst (PL-300)
Build quick, effective and interactive visuals, reports and dashboards to analyse your data.
- Data Analytics
T-SQL Querying Fundamentals (DP-080)
Learn the Transact SQL language to query SQL Server or Azure data sources
- Databases
Python Fundamentals
Attain a solid foundation of Python for developing software solutions
- Python
Certification
The knowledge from this training aligns with these certifications.
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