Explore end-to-end analytics with Microsoft Fabric
- Explain what Microsoft Fabric is and why it unifies analytics workloads
- Identify Fabric workloads and map them to common data roles
- Describe how OneLake enables AI-powered analytics
Discover and connect to data in OneLake
- Explain how OneLake provides unified storage across Microsoft Fabric
- Browse and discover data using the OneLake catalog
- Create shortcuts to connect to data without duplication
- Discover streaming data in Real-Time hub
- Exercise: Discover data in OneLake
Get started with lakehouses in Microsoft Fabric
- Describe the core features and capabilities of lakehouses
- Create a lakehouse and ingest data
- Query lakehouse data using SQL and Spark
- Connect Power BI to lakehouse data with Direct Lake
- Exercise: Create and query a lakehouse
Get started with data warehouses in Microsoft Fabric
- Describe data warehouse concepts and dimensional modelling fundamentals
- Create tables and load data into a Fabric warehouse
- Query and transform data using T-SQL and the visual query editor
- Model warehouse data for reporting and downstream consumption
- Exercise: Analyse data in a warehouse
Get started with Real-Time Intelligence in Microsoft Fabric
- Describe real-time analytics, events, and streams
- Explain how Real-Time Intelligence components work together
- Write Kusto Query Language queries to select, filter, and aggregate streaming data
- Explain how Real-Time dashboards and activator complete the real-time analytics cycle
- Exercise: Get started with Real-Time Intelligence
Choose data stores in Microsoft Fabric
- Identify common analytics workloads and the most suitable data store for each
- Compare lakehouse, warehouse, and eventhouse options
- Recommend the appropriate data store for specific business scenarios
Design dimensional models for analytics in Microsoft Fabric
- Explain how dimensional models support the curated analytics layer
- Design fact tables with appropriate grain and measures
- Design dimension tables using surrogate keys and denormalised attributes
- Select slowly changing dimension patterns for evolving data
- Exercise: Design and implement a star schema
Transform data using Dataflows Gen2 in Microsoft Fabric
- Explain when Dataflows Gen2 should be used instead of code-based approaches
- Apply transformations and configure output destinations
- Optimise performance using query folding
- Exercise: Transform data with Dataflows Gen2
Transform data using notebooks in Microsoft Fabric
- Describe Fabric notebooks and Spark-based processing
- Transform data using PySpark and Spark SQL
- Write and optimise Delta tables for analytics workloads
- Exercise: Transform data with notebooks
Transform data using T-SQL in Microsoft Fabric
- Explain the differences between T-SQL and Spark SQL workloads
- Create reusable objects for business logic and automated loading
- Implement loading patterns for dimensional models
- Exercise: Transform data with T-SQL
Create DAX calculations in semantic models
- Differentiate calculated tables, calculated columns, and measures
- Explain row context and filter context
- Create measures using advanced DAX patterns and iterator functions
- Exercise: Create DAX calculations
Design semantic models for scale in Microsoft Fabric
- Select storage modes based on performance and data freshness requirements
- Design efficient star schema relationships
- Create scalable calculation patterns
- Configure settings for enterprise-scale consumption
- Exercise: Design a semantic model for scale
Optimise semantic model performance
- Use Performance Analyzer to identify bottlenecks
- Optimise DAX calculations
- Reduce cardinality to improve efficiency
- Implement aggregations for large datasets
- Exercise: Diagnose and fix a slow report
Enforce semantic model security
- Implement row-level security using static and dynamic DAX filters
- Apply object-level security to tables and columns
- Test security configurations and manage role membership
- Explain how security affects AI-enabled consumption scenarios
- Exercise: Implement row-level security for a semantic model
Manage the semantic model development lifecycle
- Create reusable Power BI assets for consistency
- Version control semantic models using Power BI Project files and Git
- Validate models programmatically using SemPy
- Deploy through pipelines and monitor refresh operations
- Exercise: Validate and deploy a semantic model
Prepare the semantic layer for AI
- Explain how AI tools consume semantic model metadata through grounding
- Design AI-ready gold layers and naming conventions
- Configure Prep for AI features, including schema, verified answers, and instructions
- Describe how semantic models connect to enterprise ontology within Fabric IQ
- Exercise: Prepare a semantic model for AI
Create an ontology with Fabric IQ
- Explain the purpose of an ontology and how it differs from a semantic model
- Identify entity types, properties, keys, and relationships
- Generate an ontology from a Power BI semantic model
- Connect ontology definitions to data sources and preview results
- Exercise: Build an ontology from a semantic model
Secure data access in Microsoft Fabric
- Describe the multi-layer security model in Microsoft Fabric
- Configure workspace roles and item permissions using least-privilege principles
- Apply granular T-SQL security to lakehouse and warehouse data
- Create OneLake data access roles for folder-level role-based access control
- Exercise: Secure data access in Microsoft Fabric
Govern analytics data in Microsoft Fabric
- Apply sensitivity labels and understand lineage propagation
- Use endorsement and documentation features to improve trust and discoverability
- Describe how governance signals influence AI readiness and behaviour
- Exercise: Govern analytics data
