Ingest Data with Dataflows Gen2 in Microsoft Fabric
Data ingestion is crucial in analytics. Microsoft Fabric’s Data Factory offers Dataflows (Gen2) for visually creating multi-step data ingestion and transformation using Power Query Online.

Lessons
•   Understand Dataflows (Gen2) in Microsoft Fabric
•   Explore Dataflows (Gen2) in Microsoft Fabric
•   Integrate Dataflows (Gen2) and Pipelines in Microsoft Fabric
Exercise – Create and use a Dataflow (Gen2) in Microsoft Fabric

Ingest data with Spark and Microsoft Fabric notebooks
Discover how to use Apache Spark and Python for data ingestion into a Microsoft Fabric lakehouse. Fabric notebooks provide a scalable and systematic solution.

Lessons
•   Connect to data with Spark
•   Write data into a lakehouse
•   Consider uses for ingested data
Exercise – Ingest data with Spark and Microsoft Fabric notebooks

Use Data Factory pipelines in Microsoft Fabric
Microsoft Fabric includes Data Factory capabilities, including the ability to create pipelines that orchestrate data ingestion and transformation tasks.

Lessons
•   Understand pipelines
•   Use the Copy Data activity
•   Use pipeline templates
•   Run and monitor pipelines
Exercise – Ingest data with a pipeline

Get started with lakehouses in Microsoft Fabric
Lakehouses merge data lake storage flexibility with data warehouse analytics. Microsoft Fabric offers a lakehouse solution for comprehensive analytics on a single SaaS platform.

Lessons
•   Explore the Microsoft Fabric Lakehouse
•   Work with Microsoft Fabric Lakehouses
•   Explore and transform data in a lakehouse
Exercise – Create and ingest data with a Microsoft Fabric Lakehouse

Organize a Fabric lakehouse using medallion architecture design
Explore the potential of the medallion architecture design in Microsoft Fabric. Organize and transform your data across Bronze, Silver, and Gold layers of a lakehouse for optimized analytics.

Lessons
•   Describe medallion architecture
•   Implement a medallion architecture in Fabric
•   Query and report on data in your Fabric lakehouse
•   Considerations for managing your lakehouse
Exercise – Organize your Fabric lakehouse using a medallion architecture

Use Apache Spark in Microsoft Fabric
Apache Spark is a core technology for large-scale data analytics. Microsoft Fabric provides support for Spark clusters, enabling you to analyze and process data in a Lakehouse at scale.

Lessons
•   Prepare to use Apache Spark
•   Run Spark code
•   Work with data in a Spark dataframe
•   Work with data using Spark SQL
•   Visualize data in a Spark notebook
Exercise – Analyze data with Apache Spark

Work with Delta Lake tables in Microsoft Fabric
Tables in a Microsoft Fabric lakehouse are based on the Delta Lake storage format commonly used in Apache Spark. By using the enhanced capabilities of delta tables, you can create advanced analytics solutions.

Lessons
•   Understand Delta Lake
•   Create delta tables
•   Work with delta tables in Spark
•   Use delta tables with streaming data
Exercise – Use delta tables in Apache Spark

Get started with data warehouses in Microsoft Fabric
Data warehouses are analytical stores built on a relational schema to support SQL queries. Microsoft Fabric enables you to create a relational data warehouse in your workspace and integrate it easily with other elements of your end-to-end analytics solution.

Lessons
•   Understand data warehouse fundamentals
•   Understand data warehouses in Fabric
•   Query and transform data
•   Prepare data for analysis and reporting
•   Secure and monitor your data warehouse
Exercise – Analyze data in a data warehouse

Load data into a Microsoft Fabric data warehouse
Data warehouse in Microsoft Fabric is a comprehensive platform for data and analytics, featuring advanced query processing and full transactional T-SQL capabilities for easy data management and analysis.

Lessons
•   Explore data load strategies
•   Use data pipelines to load a warehouse
•   Load data using T-SQL
•   Load and transform data with Dataflow Gen2
Exercise: Load data into a warehouse in Microsoft Fabric

Query a data warehouse in Microsoft Fabric
Data warehouse in Microsoft Fabric is a comprehensive platform for data and analytics, featuring advanced query processing and full transactional T-SQL capabilities for easy data management and analysis.

lessons
•   Use the SQL query editor
•   Explore the visual query editor
•   Use client tools to query a warehouse
Exercise: Query a data warehouse in Microsoft Fabric

Monitor a Microsoft Fabric data warehouse
A data warehouse is a vital component of an enterprise analytics solution. It’s important to learn how to monitor a data warehouse so you can better understand the activity that occurs in it.

Lessons
•   Monitor capacity metrics
•   Monitor current activity
•   Monitor queries
Exercise – Monitor a data warehouse in Microsoft Fabric

Understand scalability in Power BI
Scalable data models enable enterprise-scale analytics in Power BI. Implement data modeling best practices, use large dataset storage format, and practice building a star schema to design analytics solutions that can scale.

Lessons
•   Describe the significance of scalable models
•   Implement Power BI data modeling best practices
•   Configure large datasets
Exercise: Create a star schema model

Create Power BI model relationships
Power BI model relationships form the basis of a tabular model. Define Power BI model relationships, set up relationships, recognize DAX relationship functions, and describe relationship evaluation.

Lessons
•   Understand model relationships
•   Set up relationships
•   Use DAX relationship functions
•   Understand relationship evaluation
Exercise: Work with model relationships

Use tools to optimize Power BI performance
Use tools to develop, manage, and optimize Power BI data model and DAX query performance.

Lessons
•   Use Performance analyzer
•   Troubleshoot DAX performance by using DAX Studio
•   Optimize a data model by using Best Practice Analyzer
Exercise: Use tools to optimize Power BI performance

Enforce Power BI model security
Enforce model security in Power BI using row-level security and object-level security.

Lessons
•   Restrict access to Power BI model data
•   Restrict access to Power BI model objects
•   Apply good modeling practices
Exercise: Enforce model security