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Converting an Application Database Into a Star Schema in Power Query
Markus Ehrenmueller-Jensen
Duration: 60 Minutes
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Toll free: 1-800-385-1607
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Overview:
Most application databases are designed to optimize inserts, updates, and transactional integrity - not reporting. Highly normalized schemas, bridge tables, status histories, and system-driven keys make perfect sense for developers but create serious friction for analysts. The result? Complex relationships, confusing DAX, inconsistent numbers, and performance issues in Power BI.
This session walks through a practical, real-world approach to transforming an operational application database into a clean, high-performance star schema using Power Query. Rather than pushing modeling problems downstream, we’ll reshape and structure the data at the transformation layer to create clear fact and dimension tables before it even reaches the data model.
You’ll see how to identify business processes, define proper grain, flatten normalized structures intelligently, and design dimensions that support flexible analysis. By the end of the session, you’ll understand how to bridge the gap between transactional systems and analytical models - turning raw application data into a scalable reporting foundation.
Why you should Attend:
Still pointing Power BI directly at your application database and hoping for the best? Application schemas are built for transactions - not analytics - and that mismatch can silently sabotage performance, accuracy, and scalability. In “Converting an Application Database Into a Star Schema in Power Query”, you’ll learn how to eliminate fragile joins, unpredictable measures, and report slowdowns by reshaping operational data into a clean, analytics-ready model inside Microsoft Power BI. Stop fighting your data model - start designing it with purpose.
Areas Covered in the Session:
- Why application databases are not reporting models
- Understanding OLTP vs. analytical design principles
- Identifying business processes and defining fact table grain
- Converting normalized structures into dimensions using Power Query
- Creating clean fact tables from transactional data
- Performance and size implications of modeling decisions
- Step-by-step demonstration: from raw schema to star schema
- Best practices for maintainable, scalable semantic models in Microsoft Power BI
Who Will Benefit:
- People who (start to) work with Power BI
Speaker Profile
Markus Ehrenmueller-Jensen is the founder of Savory Data, with a career spanning project leadership, data engineering, and business intelligence architecture since 1994. He holds degrees in software engineering and business education and serves as a professor of databases and project engineering at HTL Leonding, a technical college. He is also certified in PL-300 (Power BI Data Analyst), DP-203 (Azure Data Engineer Associate Certification), DP-600 (Fabric Analytics Engineer Associate), and DP-700 (Fabric Data Engineer Associate).
Markus actively contributes to the global data community, speaking regularly at international conferences such as SQL Bits in London, Power BI Next Step in Copenhagen, Data Saturdays throughout Europe, and SQL Days. He co-founded SQL PASS Austria in 2013 and the Power Platform User Group Austria in 2016; both organizations merged in 2021 to form Data Community Austria. Since 2014, he has organized Data Community Austria Day in Vienna, fostering knowledge sharing among data professionals. In recognition of his technical leadership and community involvement, Markus has been honored as a Microsoft Data Platform Most Valuable Professional (MVP) since 2017.
In addition to his speaking engagements, Markus contributes articles to reputable journals and has authored the book "Data Modeling with Microsoft Power BI," published in June 2024.