Module 1: Introduction to Microsoft Fabric
Lesson 1: Overview of Microsoft Fabric
- What is Microsoft Fabric?
- Key components (OneLake, Data Factory, Synapse, Power BI, Data Activator)
- How Fabric unifies data workloads (Data Engineering, Data Science, Data Warehousing, Real-Time Analytics)
Lesson 2: Microsoft Fabric Architecture
- Unified SaaS experience
- OneLake (the “OneDrive for data”)
- Workspaces, capacities, and roles
Lesson 3: Licensing & Pricing
- Fabric capacity (F-SKUs)
- Trial vs. paid options
- Resource allocation best practices
Module 2: Getting Started with Microsoft Fabric
Lesson 1: Setting Up a Fabric Environment
- Creating a Fabric trial
- Navigating the Fabric portal
- Understanding workspaces and permissions
Lesson 2: Introduction to OneLake
- What is OneLake?
- Storing and organizing data (Delta Parquet format)
- Shortcuts (unifying data across sources)
Lesson 3: Hands-on Lab
- Create a Fabric workspace
- Upload sample data to OneLake
- Explore the Fabric interface
Module 3: Data Engineering in Microsoft Fabric
Lesson 1: Data Pipelines & ETL
- Building data pipelines
- Copy activity, transformations
- Dataflow Gen2 vs. traditional Dataflows
Lesson 2: Spark-Based Data Engineering
- Spark jobs in Fabric
- Notebooks for data processing
- Delta Lake integration
Lesson 3: Hands-on Lab
- Ingest data from a source (e.g., CSV, SQL DB)
- Transform data using Spark notebooks
- Store processed data in OneLake
Module 4: Data Warehousing with Synapse Data Warehouse
Lesson 1: Introduction to Fabric Data Warehouse
- Differences between traditional SQL DW and Fabric DW
- T-SQL support
- Direct Lake mode (Power BI integration)
Lesson 2: Designing a Data Warehouse
- Creating tables, views, stored procedures
- Optimizing performance
- Partitioning and indexing
Lesson 3: Hands-on Lab
- Load data into a Fabric warehouse
- Write SQL queries for analytics
- Connect Power BI for reporting
Module 5: Real-Time Analytics & Data Science
Lesson 1: Real-Time Analytics (KQL Database)
- Streaming data ingestion
- Kusto Query Language (KQL) basics
- Eventhouse vs. traditional databases
Lesson 2: Data Science in Fabric
- Machine learning with Fabric notebooks
- Integrating with Azure ML
- AI-powered insights (AutoML, Copilot integration)
Lesson 3: Hands-on Lab
- Stream IoT data into Fabric
- Analyze real-time data with KQL
- Build a simple ML model
Module 6: Business Intelligence with Power BI in Fabric
Lesson 1: Power BI & Direct Lake Mode
- What is Direct Lake?
- Performance benefits over Import/DirectQuery
- Semantic models in Fabric
Lesson 2: Building Reports & Dashboards
- Creating Power BI reports in Fabric
- Sharing and collaboration
- Row-level security (RLS)
Lesson 3: Hands-on Lab
- Connect to a Fabric dataset
- Build an interactive Power BI report
- Publish and share insights
Module 7: Data Governance & Administration
Lesson 1: Security & Compliance
- Role-based access control (RBAC)
- Sensitivity labels
- Data lineage and catalog
Lesson 2: Monitoring & Optimization
- Capacity metrics
- Query performance tuning
- Cost management
Lesson 3: Hands-on Lab
- Apply sensitivity labels
- Monitor Fabric usage
- Optimize a slow-running query
Module 8: Advanced Topics & Integration
Lesson 1: CI/CD & DevOps for Fabric
- Git integration
- Deployment pipelines
- Best practices for version control
Lesson 2: Data Activator (Real-Time Actions)
- Triggering workflows based on data
- Use cases for alerts & automation
Lesson 3: Final Project
- End-to-end scenario: Ingest data → Transform → Model → Visualize → Trigger actions