Power BI, Azure data stack and SQL Server trainer at U2U
SQL Server, Business Intelligence en kunstmatige intelligentie trainer bij U2U
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,...)
Area of Expertise
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 (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.
Azure Synapse Analytics Serverless (ASAS) databases do not contain data, only external tables which point to files holding the data. Yet they are able to solve many of your big data challenges, while you only pay for what your query.
In this demo-heavy session you will learn how to explore large volumes of data, create a logical data warehouse and see how ASAS can be used for data transformation.
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.
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.
Azure Synapse Analytics makes data engineering easy. From extracting data from the source systems, transforming the data into bronze, silver or gold data all the way to hosting the data such that your business users can access them using familiar tools such as Excel and Power BI... Synapse Analytics brings it all together in a single environment.
See it all in action in this demo-rich introduction!
Azure Machine Learning plays a central role in the modern data warehouse: discovering useful patterns in all the collected data is essential in advanced analysis.
To get started with Azure machine learning you don't need to be an expert: Microsoft provides ready-made models that can be used immediately (e.g. Cognitive services). For other business problems (e.g. churn) Microsoft provides a template, but we need to tune this template to our business data. And for some analysis problems we need to build machine learning solutions from scratch.
In this demo-oriented session you will see these three ways of working with Azure Machine Learning demonstrated.
You’ve probably already seen that R icon in the Power BI GUI. It shows up when creating sources, transformations and reports. But the ugly textbox you got when you clicked upon those icons didn’t encourage you to proceed? In this session, you will learn just a few basic things about R that will greatly extend your Power BI data loading, transformation and reporting skills in Power BI Desktop and the Power BI service.
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...
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.
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 (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.
Before data can be analyzed it must first be converted and copied into a format and a location where the analysis is the easiest. ETL tools (Extract, Transform, Load) make this possible. But if the data is needed in the cloud, it’s best to run the ETL in the cloud as well.
Azure Data Factory is a cloud-based ETL tool, allowing data to be copied from on-premises as well as cloud based locations. Transformations can be hand-coded, but recently Microsoft added data flows to the product, which allow you to do data transformations without the need to write custom code.
In this session you will see the product in action and learn how to get started building your own flows.
An easy way to get started in the field of machine learning is with Automated Machine Learning, which is part of the Azure Machine Learning Service. This technology helps you build good quality machine learning models for the mainstream tasks of classification, regression and forecasting.
This demo-oriented session guides you through the process of preparing data, training and deploying models with Automated ML. Also along the way the terminology will be explained such that the session can also be attended by people with little or no machine learning background.
AI and machine learning are powerful tools! But as with all powerful tools you have to use them to solve the right problem: Even the most powerful hammer cannot do what a simple screwdriver can.
The aim of this talk is to help you identify business opportunities in which machine learning can help.
You will see four broad types of business problems for which machine learning can be the right tool for the job.
The next step will be to find answers to these questions. Azure provide many different ways to apply machine learning. In this session you will see demos of 4 different Azure services which can help you answer the questions: Cognitive services, ML Studio, ML Services and Databricks.
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!
Data governance with Microsoft Purview
Azure Data Factory, your data pipeline in the cloud
Azure Machine Learning in Stream Analytics
Power BI, Azure data stack and SQL Server trainer at U2U