Implement Analytics Solutions Using Microsoft Fabric

Implement Analytics Solutions Using Microsoft Fabric Course Content

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