This workshop will provide participants with the knowledge and tools needed to create a enterprise-ready Microsoft Fabric Lakehouse from scratch. The workshop covers everything from getting started gathering data from different sources, transforming the data into an analytical model, securing access, and monitoring it’s performance. As Fabric provides many different methods for performing these tasks, we will cover a variety of different development tools, including using shortcuts, copying data, pipelines, Data Flow Gen 2, notebooks, and explain which is the best choice in a given situation. Participants will practice the steps in hands on exercises using medallion architecture to transform the data into an analytical lakehouse, which can be used for ad-hoc querying, and of course as a source for Power BI reports. Participants will learn how to provide ongoing maintenance, security and monitoring of the lakehouse to ensure it is an enterprise level solution. The workshop experience and examples will provide participants with the knowledge needed to implement the techniques to create their own lakehouse.
By the end of the session, participants will not only understand the technical steps involved but also when and why to choose a lakehouse architecture for their organizational data needs.

Agenda Breakout
Lakehouse architecture and capabilities in Fabric
• Overview of all you can do with a Lakehouse
• Lakehouse physical structure and One Lake
• Parquet and Delta Lake
• Architectural reasons for including a lakehouse
Developing a Lakehouse with Medallion Architecture
• Process Description
• Workspace Organization
• Component organization
Loading Data into a Lakehouse
• Using the copy command
• Shortcuts
• Data Exploration in the Lakehouse with SSMS and Azure Data Studio
Implementing optimal Lakehouse design
• Dimensional modeling
• Transforming data with Pyspark and Spark SQL
• Data flow Gen2

Using a Fabric Lakehouse
• Power BI Direct Lake
• Loading data to a Data Warehouse
• Sharing
• Security
• Monitoring and Performance
• Default and other semantic modeling