Tom Martens's Speaker Profile @ Sessionize

Schlaue Berichte mit Power BI bauen

In dieser Session stellt euch Markus schlaue Funktion von Power BI Desktop vor, die es entweder sehr leicht machen, neues in den Daten zu entdecken und/oder die Daten mit hilfreichen Informationen anzureichern. Und das beste: Alle gezeigten Funktionen sind in der kostenlosen Version von Power BI verfügbar - ihr könnt diese also sofort nutzen, auch wenn ihr nicht Zugang zu einer Pro oder Premium Lizenz habt.

Q&A with Power BI MVPs

Come and join us for a panel discussion with the Power BI MVPs and experts. The team will answer your questions.

Microsoft Fabric, how to keep your items and data safe

Microsoft Fabric will change the analytical landscape, help solve the most demanding analytical challenges, and provide deep insight derived from our data assets. I assume we have heard, read, or seen these slogans: “Unified analytics fabric - End-to-end analytics data fabric - From the data lake to the business user.” Some might call me biased because I’m a Microsoft Data Platform MVP, but I’m convinced these slogans are true.

Nevertheless, we had access to the early bits and bytes of Fabric before the public preview, we have been working (to be honest and precise: we have been experimenting) with Fabric for more than a year. Next to being able to tackle the most demanding analytical challenges, it’s also essential to keep your items safe, meaning reports, notebooks, dataflows, or pipelines. It’s necessary to avoid unwanted changes or deletions. Of course, it’s not just the items, it’s also the data that we have to keep safe. Now, there is more than the RLS we know from our Power BI datasets.

If you are a data architect, a Power BI/Fabric Admin, or a Fabric workspace owner. If you are already working with Fabric or planning to work with Microsoft Fabric, I will show you how to keep your items and data safe.

Microsoft Fabric - Code first with notebooks and Python

Since the advent of notebooks inside Microsoft Fabric, more capabilities for solving our data engineering tasks have entered the stage. Next to dataflows (Gen2), creating data pipelines based on Azure Data Factory is possible now. However, my favorite method to tackle data engineering challenges has been notebooks. However, I do not use Python and PySpark for data engineering tasks alone. I also use Python to extract data from REST APIs. In this session, I demonstrate how I use Python to extract data from REST APIs, including using secrets stored inside an Azure Key Vault. Next, I will showcase how common and not-so-common data cleansing and transformation tasks can be tackled using Python and PySpark.

This session will introduce Python for data engineering but will also cover advanced techniques like user-defined functions, method chaining, and package management. All examples will be available for download from a public git repo.

Also, this session is an introduction to data engineering using Python, especially PySpark. Some experience using programming languages will be helpful to follow every aspect covered in this session.

Master Admin and Governance for Microsoft Fabric

Power BI Administrators have become Fabric Administrators from one day to another. Suddenly, there was Microsoft Fabric bringing a wealth of new features to the service, which have different requirements from an admin, governance and monitoring perspective. Now, we also incorporate data engineers, data scientists and many other roles into our ecosystem and internal community - there were we've worked with data analysts over the past years primarily.

In this table talk, four (5!) Microsoft Most Valuable professionals will discuss the admin and governance best practices combining your Power BI ecosystem with Microsoft Fabric platform solutions.

Topics that will be covered:
- Setting up domains in Fabric
- Workspace architecture
- Premium capacities versus Fabric capacities
- Capacity setup and monitoring
- New tenant configurations to consider and their impact
and much more! Your questions decide the direction of our discussion! Come and ask all your burning questions, whether you're an admin yourself or just curious the new world with Fabric and related controls.

I can, I want, I must not - an overview of the current state of Access Management in Fabric

This session provides an overview of the current state of access management in a Microsoft Fabric-enabled environment. Without any question, Microsoft Fabric will change the analytical landscape of analytical platforms. The possibilities of Microsoft Fabric seem limitless. I assume that fabric will become for analytical platforms what Power BI has become self-service Business Intelligence solutions (to be honest - Power BI is so much more already than a data visualization tool).

Nevertheless, a Fabric workspace is more complex than a Power BI workspace. Of course, it’s still a container able to store artifacts, adding compute capabilities to them. But now we must know that many more personas contribute to the workspace content. More personas are involved in creating a “Fabric Solution” than in the good old Power BI days. This session is not only about safekeeping the artifacts from accidental harm in a workspace container but also about granting access or denying access to the data.

In this session, I will showcase some use cases, and life demos of how access can be granted, or not to forget denied.

Fabric will solve our analytical challenges, but how do we organize it?! (reloaded)

Microsoft Fabric will change the analytical landscape, help solve the most demanding analytical challenges, and provide deep insight derived from our data assets. I assume we have heard, read, or seen these slogans: “Unified analytics fabric - End-to-end analytics data fabric - From the data lake to the business user.” Some might call me biased because I’m a Microsoft Data Platform MVP, but I’m convinced these slogans are true.

Nevertheless, we had access to the early bits and bytes of Fabric before the public preview, we have been working (to be honest and precise: we have been experimenting) with Fabric for more than a year. Still, we did not make up our minds about how we would organize working with Fabric. The reasons are e.g., is a Fabric solution made from a single workspace (not counting the workspaces from deployment pipeline(s)), or are there more workspaces contributing to the solutions? We also do not know how to organize the solutions around the new Fabric capacities. Will a solution “own” multiple capacities, or only one or two, and share compute power with other teams, utilizing the idea of the share economy?

If you are a data architect, a Power BI Admin, or a Power BI Developer who is already working with Fabric or you are planning to work with Microsoft Fabric, I will share some of our findings, experiences, and feelings about Fabric. Be aware that I cannot call our current thinking “best practice.” When I submitted this session, we did not use Fabric in production (most probably, this will change in Q2 2024), but we are preparing to use it. This session is not about the latest bits of configuring Spark compute, but it helps to understand some intricacies of the Fabric workspace. To follow along it will help to have an idea about the differences between PaaS and SaaS services, I will provide a short introduction, though.

Declarative data visualization using deneb

Data visualization is crucial when we want to share insights with our co-workers. But sometimes, the default visuals of Power BI and even the custom visuals available on app-source are not providing the communication I need. Creating R or Python script visuals comes with the cost of losing interactivity. Writing my own custom visual requires a lot, using IDEs like Visual Studio Code, using libraries, and compiling my code; this can be overwhelming.
One of the most powerful custom visuals available on app-source is deneb. Deneb embeds one (maybe two) of the most powerful javascript data visualization libraries vega-lite and vega. Deneb allows me to focus on the data visualization task using declarative javascript without the burden of installing 3rd party tools and demanding unfamiliar things. This session provides an introduction to deneb, its possibilities, and how templates can be used/shared throughout your organization.

Data visualization using Deneb (meaning Vega and Vega-Lite)

No matter what your data specialty is (data engineering, data modeling, etc.), in the end, data has to be "communicated" to the users of a Power BI report. Data visualizations are used when communicating data to users. Using Power BI, this can be done quickly because Power BI provides a large number of build-in data visualization types.
Sometimes, these built-in data visualizations are insufficient, whether a particular formatting requirement or a complex visual analytical method is required. But even then, these gaps can sometimes be filled using one of the many custom visuals available. One of my favorite custom visuals is Deneb (probably my most favorite custom visual). Deneb is not built to solve a specific data visualization task like a sunburst chart. Instead, Deneb has packaged two of the most powerful data visualization libraries, Vega and Vega-Lite.

Next to a short introduction to Deneb, Vega, and Vega-Lite, this session explains important concepts like layers (the superpower that allows layering bubbles on top of rectangles) and scales, the mapping of different value ranges or the mapping of a categorical variable like the product to a color. Practical examples and demos from this session, these examples will become available for downloading.

This session addresses every Power BI Report Designer and Power BI Developer who wants to bring their data visualizations to the next level.

Data mesh, data product, data WTF with Microsoft Fabric

For decades I've been designing, architecting, and implementing analytical solutions using MSFT data technologies, and I'm still not bored.
I consider the current times being the most exciting ones. New products/companies promising to solve analytical challenges easily are entering the stage almost every day. But what I consider even more important, not to say challenging is the advent of new concepts. Whereas I can ignore a new product easily to avoid re-training hundreds of users and other aspects like becoming a Junior again, new concepts are a different beast.
I consider concepts the building blocks that fuel any business or technology-focused initiative. The data warehouse concept is about creating a single point of truth (next to some other things). The concept of a lake house is about - what?
Ignoring a concept can become the root cause of demise, or as I put it sometimes: death by arrogance.

In this session, I will provide an overview of current concepts like data product, data contract, data lake house, and many other data ... concepts, why I consider some of them more important than others, compare concepts with similar approaches from the past, and of course: I will provide ideas how these concepts can be implemented using Microsoft Fabric and why they should - from a business perspective.

Composite Model - What works and what doesn't (EN)

Since the introduction of Composite Models in November 2018, one of the most asked for enhancements has been the support of Power BI datasets and Azure Analysis Services data models.
Since December 2020, this feature was released for public preview and is causing a lot of buzz.
This session provides an overview of this feature, explains terms (like islands), and lists aspects that have to be considered during implementation.

Power BI Service Administration and beyond (meaning it's also about data culture, data governance)

During this table talk Benni De Jagere, Nicky van Vroenhoven, Stepán Resl, and Tom Martens will talk about their experience when it comes to administering the Power BI Service. The service that helps our users being successful by getting insights from data to make impactful decisions.

Over the last couple of years, we have learned that being a Power BI Service administrator is much more than just turning on or off the Power BI Admin settings (no matter how important these settings are).

The focus of this table talk is not the Admin settings, as we Power BI admins are impacting the data culture and ensure that we will meet the requirements of data governance and compliance rules.

In this session, we will share what's important to our clients and the organizations we work for, and why this is important. Next to that, we will try to answer all the questions you bring to us.

Power BI Data Modeling - There is more to the star schema than you might think

We all have heard that Power BI works best if we design the dataset following the star schema paradigm. But creating a star schema comes with a price. We spent more effort on the data shaping, and sometimes it requires some mind-boggling thinking to identify the dimension tables and the fact tables.
This session explains the price we have to pay if we are not creating a start schema and provides a glimpse into aspects of advanced data modeling. Advanced data modeling comes into play when we create a model that takes away complexity from our DAX measures and supports the strength of the vertipaq engine.

Microsoft Fabric (aka Power BI) tenant settings - do I care or not?

There is only one constant about Power BI. Since the release of the Power BI Service in July 2015, it has been growing and evolving into one of the most powerful analytical platforms. From my perspective, the advent of Fabric, meaning the appearance of new powerful analytical capabilities like data warehousing and data science inside the Fabric workspace (fka Power BI App workspace), marks the dawning of a new era. Still, when I’m talking to like-minded people who are facing administrative tasks regarding the Power BI Service, I often hear: “This is boring; I’m not an administrator; I’m an artist, a DAX ninja, an M princess, or a data modeling goddess - I do not care!”, or something similar.

I started my career as a Power BI/Fabric Sherpa, more or less with the advent of Microsoft Analysis Serves Tabular, and yes, I love DAXing, data modeling, and creating large data architectures, but I consider the tenant settings not boring. Quite the contrary, being a Power BI/Fabric sherpa means, I help Power BI users (Fabric users) to be successful on their data journey. This includes keeping the assets safe, making sure the capacities are properly utilized, and also making sure that the pace of the journey matches the fitness of my colleagues. One important aspect to be successful on this joint journey is the proper configuration of the Tenant settings.

If you are a Power BI Developer who must administer the Power BI Service, or you are starting your life as a Power BI Administrator by joining this session, in this session, you will learn how tenant settings help to keep your assets safe, how you can adapt tenant settings to match the growing data literacy of your colleagues. But even if you are experienced with the Admin Tenant setting, this session might reveal something new, because I will also show how to monitor and track the tenant settings, of course using Fabric.

Data modeling - thinking out of the box

It's a good idea to stick to the star schema paradigm whenever we create a data model inside Power BI or Analysis Services Tabular. Dimensional modeling is key to the analytical power and the performance of our data model. But sometimes, sticking to a concept prevents us from finding creative solutions for many problems. In this session, I will showcase data modeling solutions for topics like the event-in-progress challenge, helping to create more simple DAX statements, and for this reason, also performing fast on top of large sets of data. But data modeling can also help to overcome challenges for data visualization. Next to the event-in-progress challenge, I will demonstrate different approaches to data modeling to help solve the "dynamic axis content" requirement.
Next to the gains we can achieve through data modeling, I will also cover the costs we have to pay. Costs don't necessarily mean money, but the time it takes to create a model (of course, time is money) and model complexity.

Power BI and large datasets

This session explains and demonstrates how the Power BI Premium Gen 2 architecture and new features help to grow the size of datasets. After introducing the Premium Gen 2 architecture, new features will be demoed. Features like hybrid tables, on-demand load, and asynchronous refresh are essential for successfully managing large datasets and enabling new solutions.

Power BI Workspace - So much more than a simple container

From time to time, I realize that the Power BI workspace is considered a nuisance that has to be selected before another fantastic report can be published to the service and shared with others. It's true, one of the ruling principles of Power BI Service, one workspace, one app, made me rethink the organization of content more than once.
I will share my findings from the last years supporting large enterprises and small organizations on using workspaces. I will explain why I consider the separation of data and content (reports and dashboards) a good idea and the XMLA endpoint's role. I demonstrate the importance of workspaces in combination with deployment pipelines and how they relate to data governance.

Power BI Service Administration and Governance – from the trenches

During this table talk Nicky van Vroenhoven, Štěpán Rešl, Nikola Ilic, and Tom Martens will share their experiences in administering and governing the Power BI Service. The service helps our users succeed by getting insights from data to make impactful decisions.

Over the last couple of years, we have learned that being a Power BI Service administrator is much more than just turning on or off the Power BI Admin settings (no matter how vital these settings are).

The focus of this table talk is not only the Admin settings, as we Power BI admins are also impacting the data culture of an organization. We can discuss aspects of sharing data inside and across organizations. But we can also talk about what has to be done to meet the requirements of data governance and compliance rules requirements.

In this session, we will try to answer all the questions you bring to us. We provide the answers based on our experience and what's important to our clients, the organizations we work for, and why this is important. We will back this up with relevant blogs and documentation as much as possible.

Sharing artifacts inside Power BI – the next level

Sharing resources is not only strengthening our social ties. The joint use of resources is vital to achieving more.
This session will explain and demonstrate more advanced topics of sharing artifacts inside Power BI! I will cover the latest features like multiple audiences, sharing datasets with external users, and how artifacts coming with datamarts can be shared. I will not just show how this works but also talk about use cases and how this can add value to your team and the entire organization. In this session, I will also provide a short recap of more simple sharing scenarios like sharing reports between users and the needed requirements because, from my experience, this is still unclear.

Power BI Universe - Mapping the hive ( EN )

In this session, an Azure-based solution will be created that maps your Power BI tenant. This solution, of course, also contains a Power BI report is part of this solution, which allows you to see the data sources that are fueling your datasets, how many workspaces exist, and a lot more.
This solution uses various Azure-based services, the Power BI Rest API, and of course Power BI Desktop for the visualization.

DAX - Musings about foundational concepts

Selected questions from the Power BI community (https://community.powerbi.com) will be discussed. All these questions are touching foundational concepts ranging from table iterator functions like SUMX, the scope of variables and of course the weird filter context. These questions provide some additional and unusual perspectives to some common and not so common problems.
Each question comes with its own slides documenting the underlying concepts and a separate PBIX file using additional explaining measures.

Composite Model - What works and what doesn't

Seit der Einführung von Composite Models im November 2018, war eins der am häufigsten nachgefragten Feature die Möglichkeit auch bestendende Analysis Services Tabular und Power BI Datenmodelle miteinander zu verbinden. Seit Dezember 2021 steht dieses Feature als Preview zur Verfügung und sorgt seit dem für viel Furore.
Diese Session gibt einen Einblick in dieses Feature, erläutert wichtige Begriffe und erklärt was bei der Implementierung berücksichtigt werden muss.

Data culture, data-enabled, data governance, data catalog, Power BI - how does this all relate

The quest for becoming a data-enabled organization that will thrive in a world where a data culture sets new standards and defines the new normal using a data catalog like Azure Pureview will become more important, not just to prevent losing focus.
This session provides meaning to some of the words used in the above sentence. It explains why mapping your data estate becomes vital in the overall theme of data governance and data strategy. A live demo demonstrates how Pureview will be setup to map a Power BI tenant and selected data sources. Next to some technical tips and tricks, some aspects on how to convince data owners to prepare "their" systems for being scanned.

DAX - Fundamental Musings

Musings about foundational concepts
Selected questions from the Power BI community (https://community.powerbi.com) will be discussed. All these questions are touching foundational concepts ranging from table iterator functions like SUMX, but also the scope of variables will be addressed. These questions provide some additional and unusual perspectives to some common and not so common problems.
Each question comes with its own Power BI Report Page(s) documenting the underlying concepts that will be available for download.

DAX - Time Series Analysis, Sequences, and some other fancy stuff

Here Time Series Analysis does not mean calculating the YTD value of a numeric value. This is about discovering the clients who have been buying 10 days in a row and how concepts of time series analysis can be used with DAX. These are the requirements to have fun attending this session: a basic understanding of the Filter Context is required, this article has to be read at least once https://mdxdax.blogspot.com/2011/03/logic-behind-magic-of-dax-cross-table.html, and it's also helpful to have a good understanding of extended tables.

Power BI - The Admin Portal

Very often the settings of the Admin portal are not touched, and left unchanged. But these settings have to be treated carefully and with respect as these settings are important for almost everything Data Governance.
This session explains the most important settings from the Admin portal of the Power BI service and explains why and how some selected settings should be configured to avoid unintentionally data loss or similar dreadful things.

Power BI - The Admin Portal (Deutsch)

Very often the settings of the Admin portal are not touched, and left unchanged. But these settings have to be treated carefully and with respect as these settings are important for almost everything Data Governance.
This session explains the most important settings from the Admin portal of the Power BI service and explains why and how some selected settings should be configured to avoid unintentionally data loss or similar dreadful things.

Use advanced Power BI techniques for text mining

In the age where we are overloaded with information it becomes essential to identify what to read first. For this reason a Power BI app helps to analyze all the emails using methods from text mining and text analysis. Using just the Power BI tool stack this solution makes email analysis versatile and easy to use.
This session provides a short introduction into text mining and text analysis and demonstrates how certain concepts can be utilized using Power BI. Throughout this session some advanced techniques from the whole Power BI stack are applied, ranging from Power Query, to data modeling topics, to advanced DAX usage, even Cognitive Services are incorporated in this analytical pipeline. Technics are used to filter out stop words from the email body and to create a whitelist to easily discover emails that contain content that is of special interest.

Tom Martens

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Tom Martens

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