Enrico van de Laar

Many modern data platform infrastructures - like those you can build in Microsoft Azure or directly use like Microsoft Fabric - offer convenience, require less technical know-how, and provide direct integrations into other services. At the same time, they also create large dependencies on their platforms that are deeply rooted and difficult to break.

Many modern data platform infrastructures - like those you can build in Microsoft Azure or directly use like Microsoft Fabric - offer convenience, require less technical know-how, and provide direct integrations into other services. At the same time, they also create large dependencies on their platforms that are deeply rooted and difficult to break. However, with the current geopolitical climate and tensions between the US and Europe, many European organisations are questioning what it really means to rely heavily on US-based technology and are looking for ways to break away from those dependencies and move towards more digitally sovereign solutions. In this session we are focusing on a practical path forward: how to migrate from Microsoft Azure and Fabric towards a sovereign data platform that is fully in control of your organisation and at the same time complies with all relevant European legislation. We will map existing Microsoft components to open-source or European alternatives, design an architecture that is open, modular, controllable, and vendor-independent, and of course, discuss the trade-offs involved in moving away from Big Tech data platforms. Instead of a theoretical discussion, this talk dives into real-world migration strategies and lessons learned from helping organisations move towards a data platform infrastructure without dependencies and fully in their own control.

Many modern data platform infrastructures - like those you can build in Microsoft Azure or directly use like Microsoft Fabric - offer convenience, require less technical know-how, and provide direct integrations into other services. At the same time, they also create large dependencies on their platforms that are deeply rooted and difficult to break.

When it comes to working with sensitive data inside any data platform environment you need to make sure you have your security and privacy measures in order. This is especially true when you move sensitive data into a cloud platform like Azure where the geographic location and the access to your data changes. Thankfully Azure provides many options that we can use to secure, monitor and protect our sensitive data. In this session we are going to explore a number of these options, ranging from authentication and authorization to row-level security and dynamic data masking. Next to these various technical measures, we are also going to explore more policy-based ones, like data sharing agreements and data governance. So, if you are already working with sensitive data in Azure - or planning to - this session will help you decide which options best fit your situation, how to get started with their implementation and measure their impact on your use-cases.

Data is one of the most valuable assets to any company right now and you can gain a corporate advantage through the insights your data can provide. Many organizations are leveraging the power of statistics, Big Data and Machine Learning on a daily basis to optimize their products or achieve their strategic goals.

Data is one of the most valuable assets to any company right now and you can gain a corporate advantage through the insights your data can provide. Many organizations are leveraging the power of statistics, Big Data and Machine Learning on a daily basis to optimize their products or achieve their strategic goals.

We live in an exciting time where data is everywhere and available to everyone. We’ve seen the power of data put to use to increase safety, for instance by assisting car drivers to brake when someone suddenly crosses the road. Another way data is put to use, is by helping us broaden our horizon by suggesting what movie or band we should check out based on our viewing and listening behavior. Many of these data-driven innovations are meant to have a positive impact on our daily lives. However, data, and the algorithms that use the data to drive innovations, are also used for less transparent or noble goals. From building psychological models on individuals–based on the things we “like” on Facebook–to influence our behavior, to deciding whether or not we should be hired for that new job we applied for. Where exactly do we, people who work with data every day, draw the line between the ethical and unethical use of data? Unfortunately, the answer to that question is not as easy as you would think. In this session, we are going to look at how data and algorithms change the way we interact with each other, buy products, and even influence who we will vote for in an election. By showing you how your data is used to influence your behavior, I hope you will be more empowered to answer the beforementioned question!

Data is one of the most valuable assets to the company. Another most valuable asset is people. Gaining a corporate advantage through the insights of your data that engineers, analysts, and data scientists provide, is the only correct way. However, the journey of implementing advanced analytics, data science solutions, or machine learning algorithms can also lead to very interesting results, side quests, or even side projects. And crazy data science v2.0 is one of these side projects that always find a funny note and different aspects of data science. Remember that algorithm that named paint colours? That's nothing compared to what you are going to witness in this session. From analysing demonic possession based on selfies taken from people during sessions, analysing how "metal" song is, and many quirky, wacky, funny examples. In this session, we will explore many of the crazy things that can be done with data science. And who knows? You might even learn a thing or two!

We live in an exciting time where data is everywhere and available to everyone. A large portion of this data consists of privacy sensitive data. And as someone that works in a role that deals with data every day, we are pretty sure you have seen your fair portion of sensitive data in the environments you work in. In the last couple of years there is a clear growth in privacy awareness among the individuals of whom you are processing privacy sensitive data, together with an increase in legislation. This not only means data privacy protection is more important than ever, but also that there are huge new opportunities for your organization to differentiate itself from its competitors. In this session we are going to explore why data privacy is so important and what you – as a data professional – can do to protect it. Because ultimately, you are in the perfect position to understand what the impact data privacy protection can have on the data and its usage. Don’t worry, we are keeping the legal bits to a minimum and instead focus on practical examples and use-cases you can directly implement inside your environment.

With SQL Server 2019, Microsoft introduced a killer feature: Big Data Clusters. They bring advanced machine learning, an in-SQL Server Data Lake and enhanced Data virtualization through PolyBase. We will take a look at what that means, how Data Virtualization differs from Data Integration, what it takes to get a Big Data Cluster (even for small Data) deployed and how it can help you solve real time issues you may have. To do so, we will not only guide you through the product itself but also through the technologies and tools like Spark, Notebooks and Azure Data Studio that it relies on, so you can leave with a call to action on how you can leverage these new capabilities in your organization today!

Data is the most valuable asset to any company right now and you can gain a corporate advantage through the insights your data can provide. However, implementing Advanced Analytics can also lead to very....interesting....results. Remember that algorithm that named paint colors? That's nothing compared to what you are going to witness in this session. From analysing demonic possession based on selfies, to analysing how "metal" a song is. In this session we will explore all the crazy things that are possible with data science. And who knows? You might even learn a thing or two!