Key-value-pair tables - often used in applications for their flexibility - typically consist of just two columns: a key (or attribute name) and a value. This format allows developers to easily add new fields without altering the table structure, making it a favorite in application development.

But what’s great for developers can be a major headache for report builders. Business Intelligence tools like Power BI thrive on structured, columnar data - where each attribute has its own dedicated column and correct data type. To get there, key-value tables must be pivoted and typed properly - a task that can be tedious, error-prone, and difficult to maintain.

In this session, Markus Ehrenmueller-Jensen shares a real-world solution he implemented for a client running in production today. He’ll walk you through a semi-automated approach for transforming key-value tables into analysis-ready datasets—both in self-service BI scenarios using Power Query, and in enterprise-grade pipelines using SQL and PySpark in Microsoft Fabric.

What You’ll Learn:
* Challenges of working with key-value data in reporting tools
* How to pivot and reshape key-value tables for analytics
* Automatically assigning correct data types for aggregated reporting
* Self-service vs. enterprise approaches in Power Query, SQL, and PySpark

Who Should Attend:
Power BI developers, data engineers, and architects working in Microsoft Fabric who need to turn flexible data structures into performance-optimized models for reporting and analysis.