Own your data. Own your skills.

In this session I guide attendees through a number of SQL Server settings that are often left at their default values but can have a significant impact. Using real-world examples, I show how targeted configuration choices directly contribute to better performance and stability of databases.

The focus is on understanding the why behind these choices, so that not only the technology is discussed, but also how it can be applied in a real-world IT environment.

In this session, attendees are introduced to wait statistics in SQL Server and learn how these built-in performance statistics provide insight into bottlenecks within a SQL Server instance. We start with an explanation of how waits arise and cover a number of common wait types. Next, we look at how SQL Server records these and how you can use this information for performance troubleshooting.

The session combines theory with practice, including analysing Dynamic Management Views and how to interpret common wait types. This gives attendees a solid foundation to better understand performance problems and optimise in a targeted way.

In this session we dive into the world of In-Memory OLTP and memory-optimized tables within SQL Server. Attendees gain insight into how this technology makes it possible to significantly accelerate database workloads by processing data directly in memory, with performance improvements that in practice can amount to a multiple of traditional solutions.

Besides the basic principles, we cover the underlying mechanics, such as data and delta files, transaction logging and concurrency models. We also cover, in practice, how to design and implement memory-optimized tables, including the use of hash indexes and the Memory Optimization Advisor.

This session focuses on designing effective indexes within SQL Server. Attendees gain insight into how indexes contribute to performance, but also which considerations and trade-offs are involved. Because although indexes have a positive effect on queries that only read data, they have a negative effect the moment data is modified.

We start by discussing the various index types and their internal workings. Next, the focus is on practical guidelines for designing, analysing and maintaining indexes, so that attendees are better able to make well-considered choices in their database design.

In this session, attendees are introduced to the Query Store, a powerful feature introduced in SQL Server 2016 that provides insight into query performance at the database level. Using real-world examples, we show how Query Store automatically captures query history and how this information can be used to analyse and resolve performance problems more quickly.

In addition, attendees also learn how they can compare execution plans and even force them via the Query Store in situations where generating an alternative execution plan leads to performance problems.

In this session we go deeper into analysing SQL Server performance using the Query Store. Where the basics of the Query Store are often familiar, the focus in this session is on actually leveraging the collected data to understand and resolve performance problems.

Attendees learn how query runtime statistics are built up and how these insights can be used in both standard reports and custom solutions. In addition, attention is paid to practical tooling, such as dashboards and scripts, with which performance analysis can be further automated.

This session, specially created for SQL Saturday edition 666, shows in a light-hearted and humorous way how we can use Azure Machine Learning to predict whether someone is possessed by a demon.

Behind the peculiar use case lies a serious introduction to machine learning, and the techniques demonstrated during the session are genuinely usable in the real world (also for classification purposes other than predicting demonic possession 😉).

A session you won’t soon forget, and the one that originally led to the Crazy Data Science sessions, which explain complex data science techniques in an accessible and humorous way and then apply them to completely useless examples.

The Crazy Data Science sessions aim to explain complex data science techniques in an accessible and understandable way, with a wink.

In these sessions – often co-presented with Tomaž Kaštrun, a Slovenian data scientist – a wide range of topics and techniques pass by. All applicable in the real world, but within the Crazy Data Science session applied to often absurd problems you didn’t even know you had.

How about, for example, automatically detecting Wally in the well-known Where’s Wally puzzles? Or an algorithm that answers the question: “If I were a character in a horror film, would I survive?”. These are just two examples of problems we solved during the Crazy Data Science sessions, but the idea is clear: by laughing together, you learn complex topics much faster!

Personal highlight: an algorithm based on OpenCV and Tensorflow that can automatically recognise my face within a mosh pit, since my wife tends to lose me during concerts and festivals. This of course had to be tested live during the session, which I think resulted in the first official mosh pit at a data conference.

This session offers an accessible and practice-oriented introduction to data science, specifically aimed at BI professionals and DBAs. Starting from familiar concepts, the step is made from traditional data analysis to a more data-science-driven approach, in which not only insight but also prediction takes centre stage.

Attendees are taken through the entire data science process: from statistical basic principles and model selection to building machine learning models in R and Azure Machine Learning. In addition, attention is paid to operationalising models, so that analytics does not remain merely experimental, but actually adds value in production environments.

Where traditional analytics workflows often revolve around moving data to external tools, this session shows how you can instead bring models closer to the data by making use of in-database machine learning in SQL Server.

Attendees gain insight into how this approach leads to better performance, less data movement and possibilities for near real-time model scoring. In addition, practical implementations are covered, such as training and running models within SQL Server and integrating Azure Machine Learning with on-premises data.

In this session, attendees are introduced to SQL Server Big Data Clusters and the role this technology plays in modern data architectures. The focus is on creating a central data platform in which various data sources come together and can be analysed in combination.

Based on the architecture and underlying technologies, such as containers and orchestration, insight is given into how SQL Server Big Data Clusters enable scalability and flexibility. In addition, we take a practical look at accessing and analysing data through, among other things, external tables and Spark integrations.

This session centres on the impact of data on our daily lives, with a focus on privacy, ethics and influence. Using relatable examples and current developments – such as social media, algorithms and deepfakes – it is made clear how data not only analyses, but can also steer behaviour.

The session combines technology with societal issues and challenges attendees to think critically about the role of data in organisations and society. The aim is to create awareness around the responsibilities involved in working with data and AI.

This session gives data professionals a practical and accessible overview of data privacy in their daily work. Starting from the growing attention to privacy and regulation (such as the GDPR), it explains why data privacy is important and which key role data professionals play in it.

The session focuses on identifying privacy-sensitive data, understanding who has access to this data and which measures are needed to protect it. In addition, it covers the importance of clear purpose limitation and how organisations can remain compliant without stifling innovation.

Finally, the session introduces privacy-enhancing technologies (such as anonymisation and synthetic data) as a way to keep working with data safely, with the balance between the usability of data and privacy taking centre stage.

This guest lecture takes attendees into the world of data privacy and ethics from a critical and societal perspective. Using the provocative proposition “data privacy is dead”, it explores how power, technology and data are intertwined.

The session challenges attendees to look beyond laws and regulations alone and to pause and consider the ethical implications of data use. Who holds power over data? What does privacy still mean in a world where data is collected and used everywhere?

Through examples and reflection, attendees gain insight into their own role and responsibility in protecting privacy and making ethically sound choices.

Applied Data Science in Azure is a one-day training that takes attendees into the world of the data scientist who works daily with Microsoft Fabric and Azure Machine Learning.

During this training, attendees gain insight into a powerful combination within Azure: Microsoft Fabric and Azure Machine Learning. We cover where and how you can get started with both services, how you build data pipelines and dataflows, analyse data and, of course, how you develop, deploy and use predictive models.

At the end of this training, attendees will have enough knowledge to deploy both Microsoft Fabric and Azure Machine Learning effectively for better decision-making and for training machine learning models within their organisation.

The one-day training consists of 6 modules and covers all the important components, tools and best practices for machine learning workloads within both Microsoft Fabric and Azure Machine Learning.

In this session you learn how to work safely with privacy-sensitive data within Microsoft Azure. From both a legal and technical perspective, it explains what you need to take into account when you bring data to the cloud.

The session starts with the most important compliance aspects, such as the GDPR and Standard Contractual Clauses (SCCs), and emphasises that technology only becomes relevant once the legal foundation is in order. Next, it zooms in on concrete measures within Azure to protect sensitive data.

Attendees gain insight into various layers of security – from subscription- and service-level measures to general best practices such as data minimisation and anonymisation.

This basic T-SQL training is intended for anyone who wants to learn how to write effective queries to retrieve and manipulate data from relational databases. The session starts with the fundamentals of SQL and relational databases and builds up step by step towards more advanced querying techniques.

Attendees learn how to select, filter, sort and modify data, but also how to perform more complex operations such as joins, aggregations and subqueries. In addition, attention is paid to more advanced topics such as functions, temporary tables and query performance.

The training is strongly practice-oriented: theory is continuously alternated with exercises so that attendees can immediately apply and deepen what they have learned.

In this training you learn how to design effective data models that align with both operational and analytical needs. The session starts with the basic principles of data modelling and normalisation, and shows how relational models arose and are applied in modern data systems.

Next, the difference between OLTP and OLAP systems is covered and why these different approaches call for different data models. Attendees get acquainted with dimensional modelling and commonly used models such as the star schema and snowflake schema. More advanced models such as Data Vault and Anchor modelling are also covered.

Besides the modelling itself, attention is paid to the broader context: how do you choose the right model, what does a data warehouse data flow look like and what role do aspects such as data quality, governance, security and privacy play. The training combines theory with practical exercises in which attendees design a data model themselves.

This intensive masterclass focuses on analysing and optimising query performance in SQL Server. The session combines in-depth knowledge of the internal workings of SQL Server with practical techniques to identify and resolve performance problems.

Attendees gain insight into the architecture of SQL Server, how queries are processed and how execution plans come about. Next, it covers various tools and methods to analyse performance, such as DMVs, Query Store, Extended Events and wait statistics.

The second part of the masterclass focuses on concrete tuning techniques, including indexing strategies, dealing with parameter sniffing, optimising parallelism and correctly applying statistics. In addition, topics such as locking, blocking and isolation levels are covered.

The masterclass concludes with best practices around performance setup, data modelling and secure SQL development, so that attendees are not only able to solve existing problems, but can also prevent future performance issues.

This guest lecture offers an accessible yet critical introduction to Artificial Intelligence (AI). The session explores what AI exactly is, how it has developed historically and which role data and algorithms play in modern applications such as machine learning and generative AI.

In addition, it pauses to consider the societal impact of AI: from opportunities in productivity and innovation to risks around bias, privacy, ethics and autonomy. Through real-world examples and interactive discussions, attendees are challenged not only to understand AI, but also to assess it critically and apply it responsibly.