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.
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Course Outline
1. Introduction to end-to-end analytics using Microsoft Fabric
• Explore end-to-end analytics with Microsoft Fabric
• Explore data teams and Microsoft Fabric
• Enable and use Microsoft Fabric
2. Discover and connect to data in OneLake
• Understand OneLake
• Browse and connect to data in OneLake
• Discover streaming data in Real-Time hub
3. Get started with lakehouses in Microsoft Fabric
• Describe lakehouse features and capabilities
• Ingest and transform data in a lakehouse
• Query and analyze lakehouse data
4. Get started with data warehouses in Microsoft Fabric
• Understand data warehouses
• Understand data warehouses in Fabric
• Query and transform data
• Model data in a warehouse
• Secure and monitor a warehouse
5. Get started with Real-Time Intelligence in Microsoft Fabric
• What is data analytics?
• Real-Time Intelligence in Microsoft Fabric
• Ingest and transform real-time data
• Store and query real-time data
• Visualize real-time data
• Automate actions
6. Choose data stores in Microsoft Fabric
• Describe analytical data store options
• Evaluate lakehouse capabilities
• Evaluate warehouse capabilities
• Evaluate eventhouse capabilities
• Case study – Choose data stores for an integrated analytics solution
7. Design dimensional models for analytics in Microsoft Fabric
• Describe dimensional schema types
• Design fact tables
• Design dimension tables
• Implement slowly changing dimensions
8. Transform data using Dataflows Gen2 in Microsoft Fabric
• Understand Dataflows Gen2
• Transform data with Power Query
• Optimize Dataflows Gen2 performance
9. Transform data using notebooks in Microsoft Fabric
• Describe notebooks in Fabric
• Shape and clean data
• Combine and aggregate data
• Write and size Delta tables
10. Transform data using T-SQL in Microsoft Fabric
• Transform data with T-SQL queries
• Create views for reusable logic
• Build stored procedures
• Implement dimensional tables
11. Create DAX calculations in semantic models
• Create calculated tables
• Create calculated columns
• Understand implicit measures
• Create explicit measures
• Use iterator functions
12. Design semantic models for scale in Microsoft Fabric
• Choose a storage mode
• Design star schema for semantic models
• Design scalable calculations
• Configure settings for scale
13. Optimize semantic model performance
• Use Performance analyzer to diagnose issues
• Optimize DAX calculations
• Reduce cardinality for better performance
• Implement aggregations
• Troubleshoot common performance issues
14. Enforce semantic model security
• Implement row-level security
• Apply object-level security
• Test security and manage roles
15. Manage the semantic model development lifecycle
• Create reusable Power BI assets
• Manage Power BI content in version control
• Manage semantic models with the XMLA endpoint
• Deploy content through stages
• Maintain and monitor semantic models
16. Prepare semantic models for AI in Power BI and Microsoft Fabric
• Explain how semantic model context guides Copilot answers
• Design a focused gold layer for AI consumption
• Select the appropriate prep for AI control
• Decide when to extend a semantic model with Fabric IQ
• Validate AI readiness
17. Understand Microsoft Fabric IQ fundamentals
• Get started with Fabric IQ
• Explore Microsoft Fabric IQ components
• Understand the ontology modeling paradigm
18. Create an ontology with Fabric IQ
• Choose an ontology creation approach
• Build an ontology manually
• Generate an ontology from a Power BI semantic model
• Connect an ontology to data
• Configure ontology relationships
• Preview the ontology
19. Secure data access in Microsoft Fabric
• Understand the Fabric security model
• Configure workspace and item permissions
• Apply granular permissions
20. Secure a Microsoft Fabric data warehouse
• Explore dynamic data masking
• Implement row-level security
• Implement column-level security
• Configure SQL granular permissions using T-SQL
21. Govern analytics data in Microsoft Fabric
• Classify and protect data in Microsoft Fabric
• Endorse and document data assets
• Monitor and manage governance
Course Details
• Familiarity with data concepts and terminology
• Experience working with data stores such as lakehouses or warehouses
• Familiarity with SQL query syntax
• Understanding of data modeling concepts such as tables, relationships, and keys
• Experience building reports in Power BI
• Familiarity with DAX syntax including measures and calculated columns
• Understanding of dimensional data modeling design concepts
• Experience designing and publishing semantic models in Microsoft Fabric
• Familiarity with DAX measures and relationships in semantic models
• Basic understanding of how AI agents consume structured data
• Experience administering Microsoft Fabric workspaces
• Familiarity with role-based access control concepts
• Understanding of data sensitivity classification and labeling
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.
For online (live) format
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