Introduction to end-to-end analytics using Microsoft Fabric
Discover how Microsoft Fabric can meet your enterprise's analytics needs in one platform. Learn about Microsoft Fabric, how it works, and identify how you can use it for your analytics needs.
- Explore end-to-end analytics with Microsoft Fabric
- Explore data teams and Microsoft Fabric
- Enable and use Microsoft Fabric
Discover and connect to data in OneLake
Browse and connect to data using Microsoft OneLake's unified storage. Discover data across workspaces with the OneLake catalog, create shortcuts to reference existing data, and explore streaming sources in Real-Time hub.
- Understand OneLake
- Browse and connect to data in OneLake
- Discover streaming data in Real-Time hub
- Exercise: Discover and connect to data in OneLake
Get started with lakehouses in Microsoft Fabric
Lakehouses in Microsoft Fabric combine data lake storage flexibility with data warehouse analytical capabilities. Learn how to create a lakehouse, ingest and transform data, and query data with SQL and Spark.
- Describe lakehouse features and capabilities
- Ingest and transform data in a lakehouse
- Query and analyze lakehouse data
- Exercise: Create a Microsoft Fabric lakehouse
Get started with data warehouses in Microsoft Fabric
Understand what a Fabric data warehouse is, why it provides full T-SQL transactional capabilities, and how to create, query, and transform data for analytics.
- Understand data warehouses
- Understand data warehouses in Fabric
- Query and transform data
- Model data in a warehouse
- Secure and monitor a warehouse
- Exercise: Create and query a warehouse
Get started with Real-Time Intelligence in Microsoft Fabric
Real-Time Intelligence in Microsoft Fabric helps you ingest, process, store, visualize, and act on data in motion to get insights from events as they happen.
- What is real-time 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
- Exercise: Get started with Real-Time Intelligence in Microsoft Fabric
Choose data stores in Microsoft Fabric
Evaluate lakehouse, warehouse, and eventhouse options to select the appropriate analytical data store for business scenarios 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
Design dimensional models for analytics in Microsoft Fabric
Learn dimensional schema types, fact and dimension table design, and slowly changing dimension patterns for analytics workloads in Microsoft Fabric.
- Describe dimensional schema types
- Design fact tables
- Design dimension tables
- Implement slowly changing dimensions
- Exercise: Design and implement a dimensional model
Transform data using Dataflows Gen2 in Microsoft Fabric
Apply low-code transformations using Power Query in Dataflows Gen2 to prepare analytical data for downstream consumption.
- Understand Dataflows Gen2
- Transform data with Power Query
- Optimize Dataflows Gen2 performance
- Exercise: Transform data with Dataflows Gen2
Transform data using notebooks in Microsoft Fabric
Use Fabric notebooks to transform data with Spark SQL and PySpark, connecting to lakehouses, warehouses, and other data stores.
- Describe notebooks in Fabric
- Shape and clean data
- Combine and aggregate data
- Write and size Delta tables
- Exercise: Transform data with notebooks
Transform data using T-SQL in Microsoft Fabric
Use T-SQL in Microsoft Fabric warehouses to transform and query data, create reusable views and stored procedures, and build dimensional tables.
- Transform data with T-SQL queries
- Create views for reusable logic
- Build stored procedures
- Implement dimensional tables
- Exercise: Transform data with T-SQL
Create DAX calculations in semantic models
Adding DAX calculations to Power BI semantic models allows you to define custom logic within your data model, enabling deeper analysis and data-driven business decisions.
- Create calculated tables
- Create calculated columns
- Understand implicit measures
- Create explicit measures
- Use iterator functions
- Exercise: Create DAX calculations
Design semantic models for scale in Microsoft Fabric
Design semantic models for scale in Microsoft Fabric. Choose the right storage mode, design star schema relationships for clarity and performance, create scalable calculation patterns, and configure settings that support large datasets and concurrent consumption.
- Choose a storage mode
- Design star schema for semantic models
- Design scalable calculations
- Configure settings for scale
- Exercise: Design a semantic model for scale in Fabric
Optimize semantic model performance
Diagnose and fix semantic model and report performance issues. Use Performance analyzer to identify bottlenecks, optimize DAX calculations, reduce cardinality, and implement aggregations to improve query speed.
- Use Performance analyzer to diagnose issues
- Optimize DAX calculations
- Reduce cardinality for better performance
- Implement aggregations
- Troubleshoot common performance issues
- Exercise: Diagnose and fix a slow report
Enforce semantic model security
Implement row-level security, object-level security, and dynamic security patterns to protect sensitive data in semantic models while enabling appropriate access for different user groups.
- Implement row-level security
- Apply object-level security
- Test security and manage roles
- Exercise: Implement RLS for a semantic model
Manage the semantic model development lifecycle
Manage semantic models through their full development lifecycle. Create reusable assets, version-control with Git, inspect and validate with the XMLA endpoint and SemPy, deploy through pipelines, and maintain with monitoring and impact analysis.
- 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
- Exercise: Manage semantic models through their lifecycle
Prepare the semantic layer for AI in Microsoft Fabric
Design gold layers, semantic models, and documentation that enable Copilot, data agents, and enterprise ontologies to deliver accurate, business-relevant insights.
- Understand what AI needs from your data
- Design gold layers with AI in mind
- Prepare a semantic model for AI
- From semantic models to enterprise ontology
- Validate AI readiness
- Exercise: Prepare a semantic model for AI
Understand Microsoft Fabric IQ fundamentals
Microsoft Fabric IQ provides a way to define business vocabulary in an ontology and bind the ontology to data sources. Learn about ontology items, data agents, Graph in Microsoft Fabric, and Power BI semantic models. Discover how ontology modeling differs from traditional analytical modeling by starting with business concepts rather than specific use cases.
- Get started with Fabric IQ
- Explore Microsoft Fabric IQ components
- Understand the ontology modeling paradigm
Create an ontology with Fabric IQ
Ontologies in Fabric IQ transform your data into a business vocabulary that everyone can understand. Learn two ways to create ontologies: building manually to understand the core components, or generating automatically from Power BI semantic models to accelerate development. Practice both approaches and learn how to connect your ontology to data sources in OneLake, including lakehouse tables and eventhouse streams.
- 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
- Exercise: Build an ontology manually
- Exercise: Generate an ontology from a Power BI semantic model
Secure data access in Microsoft Fabric
Implement workspace roles, item permissions, and OneLake security roles to control data access in Microsoft Fabric.
- Understand the Fabric security model
- Configure workspace and item permissions
- Apply granular permissions
- Exercise: Secure data access in Microsoft Fabric
Secure a Microsoft Fabric data warehouse
Learn how to protect sensitive data in a Microsoft Fabric warehouse using dynamic data masking, row-level security, column-level security, and SQL granular permissions.
- Explore dynamic data masking
- Implement row-level security
- Implement column-level security
- Configure SQL granular permissions using T-SQL
- Exercise: Secure a warehouse in Microsoft Fabric
Govern analytics data in Microsoft Fabric
Implement Fabric-native governance practices including data classification, sensitivity labels, endorsement, and documentation. Ensure data assets are trustworthy and governed for both human and AI consumption.
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- Classify and protect data in Microsoft Fabric
- Endorse and document data assets
- Govern data for AI consumption
- Exercise: Govern analytics data in Microsoft Fabric
