Introduction
Introduction to data analysis and Microsoft Fabric
()
Introduction to Microsoft Fabric components
()
Lakehouses, data warehouses, notebooks, dataflows, and more
()
Fabric admin tasks, admin center, and managing user access
()
Lab: Enable a Microsoft Fabric license
()
1. Data Warehousing and SQL Fundamentals
Understand relational databases and SQL fundamentals
()
Lab: SQL commands to understand relational databases
()
Design and implement data warehouses in Microsoft Fabric
()
ETL data into the Microsoft Fabric data warehouse
()
Load data using data pipeline, Dataflow Gen2, and T-SQL
()
Monitoring and managing data warehouses
()
Lab: Create a data warehouse and load and ingest data into it
()
2. Data Ingestion and Preparation
Data ingestion using Dataflow Gen2 and data pipeline
()
Create and manage data pipelines for efficient data movement
()
Use Dataflow Gen2 for data preparation and transformation
()
Best practices for data ingestion and preparation
()
Lab: Prepare the data using Dataflow Gen2 and data pipeline
()
3. Lakehouses, Semi-Structured Data, and PySpark
Introduction to lakehouse architecture in Microsoft Fabric
()
Store and manage semi-structured data in lakehouses
()
Work with Delta Lake tables for efficient data management
()
Use PySpark for data transformation and analysis
()
Lab: Prepare the data using PySpark
()
4. Medallion Architecture in Microsoft Fabric
Understand the concept of medallion architecture
()
Design and implement medallion architecture for data management
()
Organize data lakes, data warehouses, and lakehouses
()
Best practices for scalable and efficient data architectures
()
Lab: Get hands-on to better understand medallion architecture
()
5. Exploring and Analyzing Data
Explore and analyze data in Microsoft Fabric
()
Use Power BI for data exploration and visualization
()
Perform descriptive and diagnostic analysis on datasets
()
Extract insights and make data-driven decisions
()
6. Power BI and Data Visualization
Introduction to Power BI and its role in data visualization
()
Create interactive reports and dashboards
()
Use Power BI for data exploration and analysis
()
Best practices for effective data visualization
()
Lab: Create interactive reports and publish to Power BI service
()
7. Designing and Building Semantic Models
The importance of semantic modeling in data analytics
()
Design tabular models for semantic analysis in Power BI
()
Implement relationships and calculations using DAX
()
Optimize semantic models for performance and scalability
()
Implement security settings in Microsoft Fabric
()
Lab: Manage sensitivity labels in semantic models and lakehouses
()
Lab: DAX Studio and Tabular Editor 2
()