Markus Ehrenmueller-Jensen
BI Architect
Vienna, Austria
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Markus Ehrenmüller-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 DP-203 (Data Engineering) and DP-600 (Power BI and Azure Data Services).
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.
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Workshop: Everything You Need To Know When Learning DAX
Abstract:
If you want to show more than simply the data provided by the data source and implement calculations in Power BI or Analysis Services Tabular you have to learn to write Data Analysis Expression (DAX). This workshop starts at entry level and jump-starts you into the world of even complex calculations within 8h. In the first module Markus will introduce the basic syntax, functions and concepts (which are best of bread, taken from Excel, SQL and MDX). Then you will learn that you can exchange complexity in the data model with complexity in DAX (and the other way around) in real world examples. Beginners usually struggle to fully understand the differences between calculated columns and measures. That’s why we will dedicate two modules on them. In the last module Markus will pick your brain with complex challenges and advanced techniques, which you can then take as template to apply on your own use cases.
Modules:
1. Syntax and Basic Functions
2. Data Model and DAX - A Symbiosis
3. Calculated Columns
4. Measures
5. Advanced Techniques
Workshop: Data Modeling in Power BI – From Zero to Hero
You have collected your first experiences with Power BI and wondering why the performance of your reports is slow (even when working with a relatively small amount of source rows)? Maybe you are suffering from complicated DAX formulas (which can have their cause in non-optimal modelling)? Or you are just curious about what’s the secret behind the model view? In any case, this full-day workshop got you covered!
We will start with the very basic foundation and we will work step-by-step from concepts and practical examples to real world problems and how to solve them.
Module “Modelling 101”: Tables, Primary Keys, Foreign Keys, Normal forms, Dimensional modelling, Kimball, Inmon, Data Vault
Module “Modelling Options in Power BI”: Relationships, Cardinality, Cross-filter direction, Data Preparation with DAX , Data Preparation with Power Query
Module “Special Dimensions”: Time & Date, Role-playing dimensions, Hierarchies
Module “Real world examples “: Budgeting, Localized data models, Composite models
Workshop: Advanced Data Visualization in Power BI
No matter where the data comes from (data warehouse, data lake, spreadsheets or any other data source), showing it in the right way is crucial for all organizations – as they strive for insights as a basis for actions. Wrong decisions during the “last mile” can ruin all the efforts done in the steps before. This workshop will give you hands-on experience on what Power BI Desktop (even in the free version!) has to offer for interactive reports and dashboards.
Module “Standard & Custom Visuals”: Table, Pie, Bar & Column, Line, Scatter, Map, R Visual, Python Visual, Custom Visuals
Module “Filter & Enhance”: Sync Slicers, Group Slicers, Sorting, Top N, Trend, Forecast, Anomalies, Insights
Module “Interactivity”: Drill-up, Drill-down, Drill-through, Tooltip, Bookmarks, Buttons
Module “Polishing”: Choose Colors, Themes, Background Image, Conditional Formatting, KPI
Don’t forget to bring your laptop to try all examples on your own.
Taming Key-Value Tables in Microsoft Fabric: From Flexibility to Reporting-Ready
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.
My Perfect Date (Table): Building Time Intelligence the Right Way
Nearly every data model involves date-related data - yes, even the ones without any romantic complications. That’s why building a proper date table is one of the most important (and most overlooked) foundations of a solid Power BI model.
While Power BI Desktop automatically creates hidden date tables by default, relying on them can lead to performance issues, limited flexibility, and unexpected behavior. Even Microsoft recommends turning this feature off unless your time-related requirements are extremely simple - and let’s be honest, they rarely are.
In this session, Markus Ehrenmueller-Jensen will walk you through everything you need to know to create and maintain the perfect date table for your data model. You’ll explore the downsides of auto-generated tables, learn when and why to mark a table as a "date table," and discover best practices for handling multiple date fields in your model.
Even better: You'll walk away with ready-to-use scripts for building robust, customizable date tables in DAX, Power Query, and SQL - so you can choose the approach that best fits your workflow and environment - so it’ll be up-to-date (pun fully intended).
What You'll Learn:
* Why you should avoid Power BI’s auto-generated date tables
* How to build a reusable, high-performing date table
* When and how to mark a table as a date table
* How to support multiple date fields and filters in your model
* Practical scripts in DAX, Power Query, SQL, and PySpark to get you started
Who Should Attend:
Power BI users, data modelers, and BI professionals who want to improve their time intelligence and eliminate common date-related pitfalls.
DAX Demystified: 5 Key Lessons Every Beginner Needs to Learn Early
DAX can feel like a paradox - simple at first glance, yet deceptively complex once you dig deeper. As Alberto Ferrari famously said, “DAX is easy, but hard.” If you've started your DAX journey and hit a few roadblocks, you're not alone.
In this session, Markus Ehrenmueller-Jensen shares five essential lessons he wishes he’d known when he first started with DAX. You’ll gain clarity on the fundamental concepts that often trip up beginners, like the critical difference between calculated columns and measures, and the game-changing role of row context vs. filter context. Markus will guide you through real examples and practical tips to help you understand and manipulate context—the heart of DAX—to create powerful, dynamic calculations.
Whether you're just getting started or already building models, this session will give you a solid foundation and a few "aha!" moments that make DAX finally click.
Who Should Attend:
Power BI users, data analysts, and Excel pros beginning their DAX journey—or anyone who's written a measure that didn’t quite work and wondered why.
Become a Guardian of the Star Schema with Microsoft Fabric
Do your visuals take forever to respond to filters?
Is DAX starting to feel more like a puzzle than a language?
Does your model view look like a tangled web of chaos?
Chances are, your data model is missing one crucial ingredient: a star schema.
In Power BI, a well-designed star schema isn’t just a best practice - it’s the key to performance, simplicity, and scalable DAX. The good news? Transforming a messy model into a clean star schema is easier than you think.
In this practical session, Markus Ehrenmueller-Jensen will walk you through common modeling pitfalls and show step-by-step how to reshape your model into a star schema using Power Query and DAX. You’ll learn how to:
* Assign attributes to fact and dimension tables
* Normalize your fact table and denormalize dimensions
* Add and configure a proper date table
* Replace blanks and cryptic flags with meaningful values
By the end, you’ll not only understand why star schemas matter—you’ll be well on your way to becoming a Guardian of the Star Schema.
Who Should Attend:
Anyone working with Power BI who wants faster reports, simpler DAX, and cleaner data models. Ideal for beginners and intermediate users ready to level up their modeling game.
A Game of Hierarchies – Hierarchies in Power BI and Fabric
Hierarchies are a cornerstone of most business data models—you’ll find them in product structures, sales territories, calendar dimensions, and geographic regions. Modeling these hierarchies effectively in Power BI is essential for building intuitive, high-performing reports.
In this session, Markus Ehrenmueller-Jensen will guide you through practical techniques for handling hierarchical data in Power BI - from building traditional hierarchies in your semantic model to implementing more complex parent-child structures. You’ll learn how to shape and transform hierarchical data using Power Query, SQL, and PySpark, and how to use DAX to navigate and calculate across hierarchical levels.
And to make things fun? We’ll be working with a sample dataset inspired by Game of Thrones—because who doesn’t love a good (data) battle for the Iron Throne?
What You'll Learn:
* How to model multi-level hierarchies and parent-child relationships
* Data preparation strategies using Power Query, SQL, and PySpark
* Writing DAX to traverse hierarchies and enable level-based calculations
* Design patterns that support scalability and usability in enterprise models
Who Should Attend:
Power BI developers, data engineers, and modelers who want to better understand how to manage and analyze hierarchical data using the full Power BI and Fabric toolset.
Do You Speak English? Localized Reports with Power BI
Even when we live in a global world your end-users might expect to get their reports in their own local language. This talk is guiding you through the available options and necessary steps to give the report user control over the language:
• Content of textual columns
• Headlines
• Currency
• Model (names of tables, columns and measures)
• Power BI Desktop and Power BI service
You will learn to extend Power BI’s data model to allow for multi-language support of column content and headlines (and how you can automate the translation of the texts with Azure Cognitive Services). I will show you how you can implement currency conversion and how to translate the model’s meta data. Finally, we look at how to change the language in Power BI Desktop in in Power service.
Self-Service AI with Power BI
Power BI Desktop is Microsoft’s free tool for self-service BI. It’s updated every single month with exciting new features and Gartner declared it since 2019 to THE leading BI Tool (which led Tableau and Qlik behind). In this full day workshop, we take a look on Power BI Desktop’s possibilities concerning Artificial Intelligence. The functionalities are reaching from simple context menus, to e. g. get an explanation for a peak in a value over time, to ingesting a self-deployed Azure Machine Learning web service.
We will touch some of the following smart features:
• Q & A
• Smart Narrative
• Quick Insights
• What-if Parameter
• Analytic Line
• Anomaly Detection
• Data Profiling
• R & Python Integration
• Smart Custom Visuals
• Key Influencer Visual
• Column by Example
• Decision Tree
• Cognitive Services
• Azure Machine Learning
The demos show both, no-code solutions and complex scripts in DAX, R, Python and M. Knowledge in those languages are helpful but not necessary.
Power BI and Big Tables: Optimizing Performance with Hybrid Approaches
When working with large datasets in Power BI, the best performance typically comes from importing all the data into the model. However, there are times when this isn’t feasible - whether due to resource limitations or the sheer time required to refresh massive volumes of data. Enter DirectQuery mode, which connects directly to your data source without importing it - but this often results in sluggish performance and frustrated users.
Fortunately, Power BI offers more than just an "Import or DirectQuery" choice. By combining techniques like dual mode, hybrid tables, incremental refresh, pre-aggregated tables, and the emerging Direct Lake storage mode, you can strike a balance between data freshness and query performance.
In this session, Markus Ehrenmueller-Jensen will guide you through these advanced features, showing you how to implement hybrid solutions that give you the best of both worlds - fast query performance with up-to-date data.
What You'll Learn:
* The trade-offs between Import mode and DirectQuery
* How to use dual mode and hybrid tables for optimal performance
* Implementing incremental refresh and pre-aggregated tables
* Leveraging Direct Lake storage mode to handle large datasets
* Practical examples for real-world hybrid scenarios
Who Should Attend:
Power BI users, report developers, and data engineers who work with large datasets and want to optimize the performance and freshness of their Power BI reports.
Markus Ehrenmueller-Jensen
BI Architect
Vienna, Austria
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