Karin Szilágyi
Senior Data & Analytics Consultant @adesso Austria | Fabric Super User
Senior Data & Analytics Consultant @adesso Austria | Fabric Super User
Graz, Austria
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Karin is a Senior Data & Analytics Consultant at adesso Austria, specializing in Microsoft Fabric, Power BI, and the modern data stack. With a strong end-to-end mindset, she focuses on breaking down complex BI challenges into practical, actionable solutions.
As one of the organizers of the Microsoft Fabric & Power BI User Group Austria, she helps to connect and grow the local data community. In her work, she combines clear data modeling with a pragmatic “let’s just try it” approach to explore - and sometimes break - new features across the Fabric ecosystem.
Karin arbeitet als Senior Data & Analytics Consultant bei adesso Austria und brennt für Microsoft Fabric, Power BI sowie den Microsoft Data Stack. Ihre Spezialität ist es, komplexe BI-Herausforderungen auf pragmatische, machbare End-to-End-Lösungen herunterzubrechen.
Abseits der Kundenprojekte vernetzt sie als Organisatorin der Microsoft Fabric & Power BI User Group Austria die lokale Data-Community. Im Alltag kombiniert sie saubere Datenmodellierung mit einer pragmatischen „Let's try it“-Einstellung: Neue Features im Fabric-Ökosystem werden auf Herz und Nieren geprüft – und wenn nötig auch mal zerlegt.
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More than just a Tool(tip): Elevating Business Context without Visual Noise in Power BI en de
Human brains have a strict limit on cognitive load, and overloaded report pages don't help. Power BI has several built-in features that can increase your report's information density without overwhelming your audience, yet they are rarely used beyond their default settings.
In this session, we'll break down how to add context right where the user is looking, making your report pages much more valuable without adding visual clutter. We'll take a closer look at:
▪ Mastering the full tooltip spectrum: from optimizing standard tooltips and building dynamic tooltip pages to understanding how your choices impact report accessibility.
▪ Designing on-demand user help and embedding documentation.
▪ Turning tabular data points into natural-language findings with native sentence formats.
▪ Embedding dynamic, contextual information directly into your visual titles.
You will leave with a clear understanding of how to use these native design tricks to add value instead of visual noise.
More than just a Tool(tip): Business-Kontext ohne visuelle Überladung in Power BI en de
Unser Gehirn kann nur eine begrenzte Menge an Informationen gleichzeitig verarbeiten, und überladene Berichtsseiten machen es nicht gerade leichter. Power BI bietet großartige integrierte Funktionen, um die Datendichte eines Berichts zu verdoppeln, ohne das Publikum zu überfordern. Doch diese Features werden selten über ihre Standardeinstellungen hinaus genutzt.
In dieser Session zeige ich, wie du Kontext genau dort platzierst, wo die User gerade hinschauen: für maximale Informationsdichte – ganz ohne visuellen Ballast. Wir sehen uns an:
▪ wie du das gesamte Tooltip-Spektrum ausreizt – von optimierten Standard-Tooltips über dynamische Tooltip-Seiten bis hin zu den Auswirkungen auf die Barrierefreiheit.
▪ wie du On-Demand-Hilfen und Dokumentationen direkt in den Bericht einbettest.
▪ wie native Satzformate nackte Tabellenwerte in leicht verständliche Sätze verwandeln.
▪ wie dynamische Informationen direkt in deine Visual-Titel einfließen.
Am Ende weißt du genau, wie du diese nativen Design-Tricks nutzen kannst, um echten Mehrwert zu bieten, statt zusätzliche visuelle Unruhe zu stiften.
Living in a Material(ized) World: Medallion Architecture with Fabric Materialized Lake Views en de
Do you enjoy managing and maintaining merge scripts to move data through your Medallion Architecture? Me neither.
In this session we'll look at how to move the burden of incremental execution from your notebook scripts to Fabric’s internal storage engine instead.
We will explore how Materialized Lake Views (MLVs) allow you to build an incremental Medallion architecture using declarative SQL queries while keeping your serving layer fully compatible with Direct Lake mode.
You will learn:
▪ What Materialized Lake Views are and how they differ from standard SQL views or manual notebook tables.
▪ How to create, chain, and refresh your first Materialized Lake Views to build a functional data tier.
▪ The practical pros and cons of using this automation versus writing traditional notebook code.
living in a Material(ized) World: Medallion-Architektur mit Fabric Materialized Lake Views en de
Hast du Lust, ständig Merge-Skripte für deine Medallion-Architektur zu pflegen? Ich auch nicht.
In dieser Session verlagern wir die inkrementelle Verarbeitung von deinen Notebook-Skripten direkt in Fabrics Storage Engine.
Materialized Lake Views (MLVs) machen es möglich: Eine inkrementelle Medallion-Architektur, komplett in deklarativem SQL – und trotzdem voll kompatibel mit Direct Lake Modus für deine Reports.
Du lernst:
▪ Was Materialized Lake Views sind und wie sie sich von klassischen SQL-Views unterscheiden.
▪ Wie du Materialized Lake Views erstellst, verkettest und aktualisierst, um eine funktionierende Datenschicht aufzubauen.
▪ Die Vor- und Nachteile von Materialized Lake Views und wann klassische Notebook-Verarbeitung die bessere Wahl bleibt.
Real-Time or Real Enough? What 'Fast' Actually Means in Fabric en
The demand for "instant data" in Microsoft Fabric presents a critical architectural choice: Real-Time Intelligence, Mirroring, or Shortcuts? Each option carries a different meaning of "real-time", with major implications for latency, cost, and complexity. We'll examine end-to-end latency from source to dashboard - because to your business users, data isn't "real-time" until they can actually see it.
In this session I'll explain how to match each option to the right scenario based on your data characteristics, your latency requirements, and how much complexity you're actually willing to maintain.
Fabric's AI Toolkit: What Each Piece Does and When to Actually Use It en
Fabric keeps shipping AI features: Copilot, Data Agents, MCP Servers, Fabric IQ, and the list keeps growing. If you've lost track of what's what, this session is the reset you need.
For each tool, we’ll break it down in detail and answer:
• What does it actually do?
• What does it need to work well?
• What are the current constraints?
• When do you reach for it over another option?
No prior experience with these specific features needed. Just bring a general idea of Fabric as a data platform and a reasonable amount of skepticism.
You'll leave with a clear picture of how these tools differ, complement each other, and fit into your architecture.
Getting Data Into Microsoft Fabric: The Right Tool for the Right Job en
One of the first questions you face in Fabric is simple: How do I get my data in? The answer is: "it depends". This session will help you understand what it actually depends on.
We'll look at Fabric's core ingestion options: Pipelines, Copy Jobs, Dataflow Gen2, Spark, and Eventstream. For each one, we'll answer:
• Who is it actually built for?
• When does it perform best?
• What are its limits, bottlenecks, and cost implications?
We’ll compare them side by side in detail, so you leave knowing which option makes the most sense for your specific situation.
Budapest BI & Analytics Forum 2026 Sessionize Event Upcoming
Data Saturday Sofia 2026 Sessionize Event Upcoming
Shift+Enter Summit 2026 Sessionize Event Upcoming
Data Saturday Croatia 2026 Sessionize Event
New Stars of Data #10 Sessionize Event
Microsoft Data Platform Meetup Groningen User group Sessionize Event
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