Nico Jacobs
Power BI, Azure data stack and SQL Server trainer at U2U
SQL Server, Business Intelligence en kunstmatige intelligentie trainer bij U2U
Brussels, Belgium
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Dr. Nico Jacobs started his career as a data mining researcher at the university of Leuven, Belgium. He joined U2U in 2004 as an instructor, author and technology strategist. His current technical expertise focuses on Power BI and Data Engineering/Data Science on Microsoft Azure. As a passionate trainer, Nico likes to inspire his students to gain a thorough knowledge on the subject. Nico regularly speaks at local and international conferences. You can see what he’s up to by following @SqlWaldorf on Twitter .
Nico studeerde informatica aan KULeuven, en behaalde vervolgens een doctoraat in het domein van machine learning bij het departement computerwetenschappen (KULeuven).
Sinds 2004 werkt hij als trainer bij U2U. Hij verzorgt voornamelijk opleidingen rond SQL Server (zowel relationele databases als de BI stack), Power BI en de Azure data stack (Data Factory, Synapse Analytics, Azure Machine Learning,...)
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What's new in T-SQL
T-SQL is 35 years old, but still new features are added to it regularly. If you want to learn how to work with time series data, learn about processing JSON data in your database, see how to do a max over rows instead of columns, check regular expressions in T-SQL or turn your SQL Database into a vector store, come and see this demo-heavy session
Using the Vector data type in Azure SQL Databases
Vectors have become a new data type in Azure SQL Databases. Although vectors can be used for all sorts of things, their most important use is storing the LLM embeddings in AI applications. To see which paragraphs of text are similar to one another, lots of vectors need to be computed, stored in a database, and then distances between these vectors needs to be computed efficiently.
For this, Azure SQL Server has a new data type, and a few functions to work with this. Learn why vectors are important, and see how Azure SQL can become the place to store and query your vectors in this demo-rich session. Even if you're not into LLM's, you will find some inspiration for the use of the Vector data type.
The Power of Paginated Reports
Power BI reports are intended for interactive use. But what if you need to produce a nice PDF, Word or printed report? That is where paginated reports come in!
These reports can have a dynamic number of pages, have better options to nest visuals inside one another, and support way more exporting options than regular Power BI reports, and the cost of being less interactive.
Come and see for yourself in this demo-rich session if paginated reports could help you build the right reports for the right audience.
The How and Why of Fabric SQL Databases
Fabric is all about BI and AI. And yet, Microsoft added a traditional, operational Azure SQL Database to the Fabric stack.
This talk walks you through the steps needed to get started with Fabric SQL Databases. But it also opens up the debate on why you would want to add an operational database to the stack. Was the lakehouse, the warehouse and the eventhouse not yet enough to store all your data?
Come and see the Fabric SQL Database in action, and hear why it might be handy for your Fabric solution as well.
Taking the first hurdle: Time intelligence with DAX
After your first successes in building a Power BI solution, some users fall into the pit of disappointment as soon as they start using DAX.
This session uses the common task of adding time intelligence to a data model to explain to people new to DAX on how it works: What is a filter context, how can CALCULATE change it, and what is the impact of expanded tables on this.
Microsoft Fabric Real-time Analytics
Telemetry data, sensor readings, logs from all these smart devices... the need for processing real-time is ever increasing. Microsoft Fabric uses Azure Data Explorer to store, index and query events. Since this is part of the Microsoft Fabric framework, it integrates with Power BI for reporting.
In this session, you will see how to setup KQL (Kusto) Databases, write Kusto queries to create KQL Querysets, and work with the code-free event streams to capture and transform incoming events.
Join this demo-rich session to see how to do event processing in action on Microsoft Fabric
KQL Know-how: Mastering the Art of Kusto Query Language
If you're developing real-time data processing solutions in the Microsoft stack, whether it's Fabric EventHouses or Azure Data Explorer, knowledge of the Kusto Query Language (KQL) is essential. In this session you will learn how to start with KQL. From basic filtering and grouping over dealing with time series till geospatial queries and visualizations: A wide range of KQL features gets demonstrated and explained.
Into BI or IT Pro? You will all love Kusto!
No matter if you're sifting through millions of sales records or just want to analyze your Azure log files: Both BI and IT Pro people suffer getting the right values out of their datasets. Microsoft introduces Kusto as a new query language which integrates with many Azure services. From Log Analytics over resource graphs all the way into the Azure Data Explorer or security threat hunting: The Kusto query language rules them all.
In this session you will see where and how this new query language can be used (and why it beats SQL big time 😊). Don't miss out this session which IT Pro's and big data people can both enjoy!
Getting started with SQL Server indexing
You got started building a solution on Azure SQL or an on-premises SQL Server. Your queries return the correct results, but sometimes they are sooooo slow...
Indexes can come to the rescue. They can speed up some of your SQL queries, but they are no magical bullet: Every index will also slow down other queries.
In this session the basics on building indexes for Azure SQL and SQL Server databases are covered: heaps, clustered indexes, non-clustered indexes, clustered columnstore and non-clustered columnstore indexes are introduced. Via lots of demos you will see their effect on selects, inserts, updates and deletes.
After attending this session you should have a better idea on how to start implementing indexes on your databases.
Develop a Fabric solution in a day
If you want to get started with Microsoft Fabric, or you already got started but were a bit daunted by all the options offered by the product, this workshop might be what you need.
You will first learn how Fabric builds on top of the concepts of domains and workspaces, which allow for decentralized administration.
Next, Task Flow shows you various data architectures, and helps you implement them. In this workshop, you will implement a medallion architecture.
The rest of the workshop follows the path laid-out in the Task Flow. The first choice is among the different options for storing data (Lakehouse, Warehouse and Eventhouse) in OneLake.
Next you will see different ways to ingest and transform data: Pipelines, Dataflow Gen 2 (Power Query), T-SQL and Spark. You will practice on using all of them, and learn about the 🙂and 😞 of each.
Once the data is of acceptable quality, you can start building reports. You will learn how to build models on top of your Fabric data, and will learn how models provide a special option to connect to your Fabric data storage, called DirectLake.
Streaming data can also be handled by Fabric. The Kusto language allows you to efficiently query both batch and streaming data and helps you set up an EventHouse. But you can also trigger processes by setting up Data Activators.
Finally, a small demo on how to get started with machine learning is concluding this journey through Microsoft Fabric.
You do not need Fabric experience to attend this workshop, but familiarity with SQL and the basics of Power BI will come in handy during the labs. An environment to practice will be provided during the day.
Getting started with SparkML in Microsoft Fabric
SparkML (aka MLlib) is a library for machine learning on the Spark framework. Crucial is the concept of Transformers and Estimators, with which parameterized pipelines can be built.
In this session you will see how to start building machine learning pipelines in Microsoft Fabric notebooks using SparkML, train a machine learning model and see how to use the resulting predictions in Power BI. Finally, you will learn about the FLAML framework for automated machine learning, and SynapseML for integration with Azure AI Services.
Deeper Dive Down DAX
If you've been building Power BI semantic models and you gained basic familiarity with DAX, you sometimes still bump into 'strange' behavior: inexplicable cyclic dependencies, measures which return weird values, performance issues, ... Or maybe you had troubles implementing the advanced calculation that you need.
In that case, it might be time to upgrade your DAX skills to the next level. In this training day you will learn some of the more advanced DAX concepts. First, you get a brief refresh on the topics of row and filter context. Then you dive deeper into the following DAX concepts:
Expanded tables
Context Transition
Iterators
Calculation Groups
Windowing functions
Visual Calculations
Context transition in DAX
The DAX language is essential when building data models in either Power BI or Microsoft Fabric. One of the primary reasons why some DAX expressions have low performance is because it performs (too) many context transitions. But even worse, sometimes context transition (or the lack of context transition) causes unwanted results for your DAX expressions.
Context transitions occur in DAX when you need a filter context while you were in a row context. It can happen explicitly as well as implicitly.
This session dives into these context transitions. After a brief recap on row versus filter context, we will explore the different scenarios in which context transitions occur, and look into the factors that can make this process more expensive to execute.
This session is intended for participants who already have a basic understanding of row and filter context in DAX
Are Visual Calculations the solution to my Power BI challenges?
Visual Calculations allow Power BI developers to compute values scoped at just one visual. This has both advantages (easier to calculate certain values) and disadvantages (new DAX to learn, no reuse of calculations).
In this session, you will see the Visual Calculations in action and learn about new DAX functions specific for Visual Calculations, such that you can judge yourself if and where this new feature can be useful in your Power BI solution
Calculation groups in Power BI
DAX allows you to write powerful measures in Power BI. But sometimes you get the feeling that part of these measures are merely an advanced copy-paste from a previous measure.
Calculation groups allow you to easily create, manage and use similar measures in Power BI.
This session illustrates what calculation groups can be used for and how to create them in Power BI Desktop and the Tabular Editor.
Azure Data Explorer
Azure Data Explorer (ADX), also known as Kusto, becomes an interesting new player in the field of analyzing large volumes of online data.
In this session you will learn for which data analysis purposes ADX is best suited, and see the complete flow (from setup over data ingestion till querying with the Kusto query language) demonstrated.
Data governance with Microsoft Purview
Where is that data stored? With the wide range of data storage options (On-prem, cloud, multi-cloud) it's far from trivial to know where all your corporate data is being kept, in which format, who's responsible? A related question is where sensitive data is being stored.
In this session you can see how Microsoft Purview (former Azure Purview) collects meta-data from on-prem as well as different cloud sources, identifies sensitive data and makes it easily searchable.
DAX in a Day
Power BI, Analysis Services Tabular and Excel Power Pivot users can improve their data models with the Data Analysis eXpression (DAX) language. Although this language looks a bit like Excel, many people struggle in writing more advanced DAX expressions.
In this one day workshop the different aspects of DAX are discussed. What are the types used in DAX? How to use DAX for creating calculated columns, tables and mainly measures? A lot of attention goes into the evaluation context: What is row context and filter context, and how do expanded tables relate to this?
A lot of DAX code will be dissected to illustrate these concepts, such that you leave with a better idea of how to use DAX for solving your business needs.
SQL Server surprises
SQL Server contains a few surprises: transactions that don’t do what most people expects, NULL values that cause queries to spit out unexpected results, data type issues and many more. These are not bugs but features, nicely documented. But hey, who reads the manual?!
In this very interactive session you can learn some SQL Server surprises, and how to avoid them in your own T-SQL code. This 1 hour session can save you from many hours of debugging...
Advanced stream analytics
In the current just-in-time world we want to analyze what is happening now, not what happened yesterday. Companies start to embrace Azure Stream Analytics, which makes it easy to analyze streams of incoming events without going into advanced coding. But for advanced analytics we need machine learning to learn patterns in your data. Azure Machine learning can do this for you. But the real beauty is that both products can easily work together.
So if you want to see how within 60 minutes we can learn patterns in streams of data and apply them on live data, be sure to attend this demo-oriented session.
Deceptive DAX
The DAX language is very powerful. Handy to compute the complex business values you need, but oh so frustrating if the DAX expression doesn't do what you expected it to do.
In this session several DAX expressions will be shown which do not always do what most Power BI users expect. Step by step these DAX expressions will be dissected and the evaluation context rules will be made clear such that you understand why the expressions behave different then expected.
This session aims at Power BI data model developers which have already a basic knowledge on writing DAX expressions and want to become more experienced in understanding the DAX evaluation context and extended tables.
Azure Machine Learning
Azure Machine Learning (ML) helps you with every aspect of the machine learning process. Learn how to manage datasets, build models automatically, graphically or with code, and deploy them as a REST service in one day!
A great way to get started in the field of artificial intelligence and machine learning is with Azure Machine Learning service. This 1 day workshop first introduces the most important concepts on machine learning in general. Then, in the next 3 modules, the different types of machine learning are demonstrated and practiced:
Automated machine learning is an easy yet powerful way to get started. The Azure ML service tries a few different approaches and reports back how well they performed.
The Designer is handy for people who want to have more control over the machine learning pipeline but do not want to become experts in computer languages such as Python or R.
Finally Python can be used to full control over the machine learning process. But even then Azure can help by logging the outcome of the experiments, provide easy to configure docker images to run the machine learning code, or in generating web services to consume the results.
Curious to see all of this in action, and even to have some hands-on experience with this? Be sure then to attend this workshop! The workshop requires no prior knowledge of machine learning nor statistics, but some familiarity with the Azure portal in general is assumed if you want to participate in the hands-on labs.
When SQL Server makes mistakes…
Sometimes things don’t work out as planned. The same thing happens to our SQL Server execution plans. This can lead to horrible slow queries, or even queries failing to run at all. In this session you will see some scenarios demonstrated where SQL Server produces a wrong plan, you will learn how to identify them and what you can do to avoid them.
You will also learn more on Adaptive Query Processing, a new feature in SQL Server 2017. This allows your SQL Server to adjust wrong plans while the plan is being executed. So, if running queries performantly is one of your concerns, don’t miss out on this session!
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Nico Jacobs
Power BI, Azure data stack and SQL Server trainer at U2U
Brussels, Belgium
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