Our agenda is live!
🕜 08:00
⭐️ LESSONS FROM THE TRENCHES: REAL-WORLD ETL/ELT HORROR STORIES AND HOW TO AVOID THEM
Moving and transforming data is a fundamental task, often fraught with challenges. This session could be a candid look at common (and often overlooked) issues in data pipelines, focusing on practical troubleshooting, error handling, and robust design patterns that have stood the test of time. It would be highly relatable and practical.
🙂 Indira Bandari
🕜 09:00
⭐️ BUILDING THE €100 DATA WAREHOUSE WITH THE AZURE DATA PLATFORM
When you watch some conference sessions, or read some blog posts, it always seems like everyone is drowning in petabytes of (streaming) data. They're proclaiming you need the fastest, best, most scalable, distributed shared-nothing (or everything?) multi-parallel system that can also set coffee. But guess what?
Not every company has to deal with a scale that businesses like Google, Amazon, or Tiktok have to deal with. Not every company needs streaming data with Kafka, Spark Streaming or Event Hubs. Not every company has complex unstructured data that you need to deal with in a data lake with tools like Databricks. Some companies have just a couple of gigabytes of data (or even less). Maybe a terabyte if we're pushing it. They don't need fancy streaming real-time dashboards, they just want to analyze their financial and sales data. It's ok if it just runs once a day. Their source systems are regular databases, maybe some Excel files and a SharePoint list.
In this session, you're going to learn how you can build a data analytics platform in Azure that is dirt cheap. We're going to cover the following technologies and patterns:
* cheap ingestion with Azure Data Factory using a metadata-driven framework, in combination with Azure Logic Apps and Azure Functions
* a data warehouse implementation in Azure SQL Database. We'll cover design best practices and scaling options.
* a Power BI model for presenting the data to the end users
In each section, we'll look at how costs can be contained. After all, we want a data warehouse which doesn't cost more than €100 per month!
It's assumed you have basic knowledge of Azure Data Factory, databases and SQL.
🙂 Koen Verbeeck
🕜 10:00
⭐️ EXECUTION PLANS ... WHERE DO I START?
SQL (the language) is not a third generation language, where the developer tells the computer every step it needs to take. It is a declarative language that specifies the required results. SQL Server itself will figure out what steps it takes to get to those results. Most of the time, that works very well.
But sometimes it doesn't. Sometimes a query takes too much time. You need to find out why, so you can fix it. That's where the execution plan comes in. In the execution plan, SQL Server exposes exactly which steps it took for your query, so you can see why it's slow.
However, execution plans can be daunting to the uninitiated. Especially for complex queries. Where do you even start?
In this session you will learn how to obtain execution plans. and how to start reading and understanding them.
🙂 Hugo Kornelis
🕜 11:00
⭐️ ACCIDENTAL DATA LIES: HOW POOR VISUAL CHOICES CAN MISLEAD
Welcome to the world of accidental data lies, where innocent-looking charts quietly twist the truth. And in today’s world, where ethical data visualisation is a hot (and important) topic, it's something we all need to watch out for.
From spreadsheet wizards to Power BI spellcasters, this session will help you create visuals that don’t just dazzle, they tell the truth and earn trust.
We’ll explore the most common (and sneaky!) ways charts mislead, from pie chart pandemonium to axis trickery, colour chaos, and the dreaded “average of averages.” You’ll see real-world examples of chart crimes, laugh at some visual disasters, and sharpen your instincts for spotting visual deception.
🙂 Juliana Smith
🕜 12:00
⭐️ IT'S ONLY A MODEL - DEMYSTIFYING ROW LEVEL SECURITY IN POWER BI
Securing data in Power BI may not involve killer rabbits or holy hand grenades, but it is a quest worthy of the Round Table. This Monty-Python inspired workshop guides attendees through the mystical (and practical) world of Row-Level Security (RLS), with special attention to the perils and peculiarities of enterprise and government cloud environments.
This workshop is designed to provide Power BI developers, ranging from novices to experienced developers, with a solid foundation to implement RLS in Power BI.
Workshop attendees will learn how to set up RLS start to finish using two approaches, covering setup in both Power BI Desktop the Service. We will tackle some common challenges, discuss model considerations and how they impact RLS, and untangle the differences between access and security in Power BI. The workshop will also address governance considerations and some of the unique challenges present within government cloud infrastructures.
🙂 Leslie Welch
🕜 13:00
⭐️ ORGANIZING YOUR DATA LAKE
In today's data-driven landscape, creating an efficient data structure for your data lake has become an indispensable part of any successful data management strategy. It's no longer sufficient to merely land your data in a data lake - it's critical to plan your data structure ahead of time to maximize utility and performance.
In this enlightening session, we will delve into the importance of pre-emptive planning for your data structure. We will explore how a well-thought-out data structure can dramatically enhance the efficiency of security, partitioning, and processing strategies.
Attendees will gain insights into best practices for structuring data and will learn techniques to leverage these structures for optimal performance. This session aims to equip you with the knowledge to harness the power of your data lake effectively
🙂 Mathias Halkjaer
🕜 14:00
⭐️ CHOOSING THE RIGHT DATABASE FOR YOUR PROJECT
Not all databases are created equal, and choosing the wrong one can cause more problems than it solves. In this talk, I will walk us through the big-picture differences between major database types and what kinds of projects they’re best suited for. From relational to document-based to key-value stores and beyond, you’ll get a clearer idea of what’s out there and how to make the right call for your next build.
This session is perfect for developers, engineers, and anyone who’s ever wondered whether they’re really using the right tool for the job. No deep dive into indexes or query optimization here: just a solid, high-level look at the options and how to think through your decision-making process.
🙂 Susan Vanderford
🕜 15:00
⭐️ DATA MODELING 101
Strong data models are essential for building reliable, scalable analytics solutions—and it all starts with understanding your modeling choices. You'll learn the core concepts of data modeling through the lens of the Microsoft Data Platform—covering Power BI, SQL Server, Azure Analysis Services, and Microsoft Fabric. We'll explore when to use normalized schemas versus the “one big table” approach, and how to transition from raw data to an optimized analytical model, called a "star schema".
Using tools and languages like SQL, Power Query, DAX, and Python, Markus will demonstrate practical techniques for designing models that support both self-service BI and enterprise reporting. You’ll learn best practices—whether you're working in SQL Server, creating semantic models in Power BI, or scaling in the cloud with Azure.
Perfect for beginners and data professionals looking to strengthen their foundation, this session will help you make the right modeling choices for performance, usability, and scalability across the Microsoft ecosystem.
🙂 Markus Ehrenmueller-Jensen
🕜 16:00
⭐️ INDEXING INTERNALS FOR DEVELOPERS & DBAS
What are the secrets to making your queries run faster? Why does SQL Server use an index for some queries and not for others? What makes a good index? How many indexes should I have? Have you ever asked these questions? When you want to understand an application you look at its core architecture. Underneath the covers SQL Server is just a C++ application. Together we will discuss how the application architecture of SQL Server works, and how to apply this logic to building the best indexes for your queries.
🙂 Bradley Ball
🕜 17:00
⭐️ DEFINING WHAT’S NORMAL — THE BASICS OF DATABASE NORMALIZATION
Determining how to efficient design database structures for a given application has always relied on the same old answer: “It depends!” So, how do you determine what level of normalization your data requires? This session will review the principles of data normalization, show the impact that normalization can have on performance and data integrity, and just as importantly, show you when it’s OK to break the rules and denormalize. We will cover some case situations where it’s better not to be normal!
🙂 Aaron Cutshall, DHA, MSHI
