Wrangling Data with Python – The Fabric Way (T. Eitelberg & T.Martens)

This one-day preconference offers a structured and hands-on introduction to working with data in Microsoft Fabric using Python. The focus is on understanding core components, applying Python in a Fabric-native context, and building reproducible, production-ready data workflows.

• We begin with the foundational architecture of Microsoft Fabric: what a Lakehouse is, how Delta Tables are used, and how Spark operates within Fabric.
• Core Spark concepts will be explained without diving too deeply into internal mechanics, providing a clear understanding of how Fabric executes workloads.
• Next, we shift to Python. After a brief recap of essential syntax, we focus on key programming principles relevant to data engineering. We also introduce essential libraries commonly used in Fabric-based workflows.
• With this foundation in place, participants will move into guided hands-on exercises. These include developing data transformation and integration processes using both Python and Spark notebooks in Fabric. We’ll compare the notebook types, discuss the characteristics of different DataFrame implementations, and explain how Compute Units (CUs) influence execution performance.
• In the next phase, we explore the benefits of combining Spark with other Fabric services, particularly Power BI. This includes an introduction to Semantic Link - demonstrating how transformed data can be connected to semantic models for reporting. Both technical integration and practical design considerations will be discussed.
• The final part of the day focuses on development workflows using Visual Studio Code - locally and in the browser. We compare VS Code-based development with Fabric notebooks, highlight useful extensions, and explain scenarios where working outside the Fabric UI offers additional flexibility.

Your trainers:
Tillmann Eitelberg (https://www.linkedin.com/in/tillmanneitelberg/)

Tom Martens (https://www.linkedin.com/in/tommartens68/)

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Host or Publisher PASS Austria