Marc Lelijveld

Is Direct Lake the answer to everything? With Microsoft Fabric, many see Direct Lake as the default choice for semantic models, but the reality is far more nuanced.

Is Direct Lake the answer to everything? With Microsoft Fabric, many see Direct Lake as the default choice for semantic models, but the reality is far more nuanced.

Fabric data agent has been available for a while, yet many implementations fall short of expectations. Not because of the technology, but because of weak design, unclear instructions, or poorly prepared data models. In this session, you’ll learn how to build high‑quality, trustworthy agents by applying proven design patterns around Agent Instructions, Data Source Instructions, and Verified Answers. We’ll explore how to structure semantic models for AI, route questions intelligently across Lakehouse, Warehouse, Eventhouse, and semantic models, and avoid the most common pitfalls that lead to inconsistent results. You’ll also see how to operationalize your agents using the Data Agent SDK for automated creation, updates, and evaluations, including golden‑record testing and diagnostics. After this session, you’ll have a clear blueprint for designing reliable, secure, and well‑adopted Fabric Data Agents—from setup to scalable success.

In the era of AI, a “working agent” is only as good as the data foundation underneath it. In this 3 hour deep dive, we’ll go from raw source data to an AI ready Microsoft Fabric Lakehouse and finish with a data agent that can answer meaningful business questions—reliably and repeatably.

Are you a data engineer, or are you working with any of them? How many times have you had the discussion about who should be building the star schema? Is it the engineer working on the data platform, or the analyst working on Power BI? Maybe the truth lies somewhere in the middle. One can simply not work without the other. But building semantic models and manually setting up connections to existing tables in a lakehouse or warehouse can take a lot of unnecessary time. In this session we will explore maximum automation to generate the semantic model as part of the data platform workloads - by using Fabric's Semantic Link and open-source Semantic Link Labs. A headstart for the analyst to focus on what really matters, adding the business logic and talking to the end user about the exact requirements! Note: This will be a code heavy session and all examples shown will only be applicable to Microsoft Fabric and semantic models.

Fabric data agent has been available for a while, yet many implementations fall short of expectations. Without the right design, the agent may struggle to align with business context, produce inconsistent outputs, or require heavy manual intervention.

A full end-to-end masterclass on AI in Power BI. During this training day, we’ll cover every corner of AI in Power BI, from preparing data for AI to bringing your Power BI AI assets to life with MCP.

Power BI Visual Calculations have been around for a while, and even though the feature hasn’t reached general availability, its use cases are expanding rapidly. Visual Calculations let you define calculations directly at the visual level, reducing the need for complex DAX and enabling faster insight creation.

If you're coming from a Power BI world, the whole Fabric thing might scare you a bit. Then suddenly, there is something called Semantic Link, which allows connections from Fabric Notebooks to read both data and meta data from your Power BI Semantic Model (dataset).

If you're coming from a Power BI world, the whole Fabric thing might scare you a bit. Then suddenly, there is something called Semantic Link, which allows connections from Fabric Notebooks to read both data and meta data from your Power BI Semantic Model (dataset). You might wonder, what's all this? How does it work and how to take benefit from it? Especially when you're not familiar yet with notebooks at all. In this session we will explore what Semantic Link is, how you as Power BI developer/engineer can make use of it and how it will strengthen your solutions in the end. Together, we will explore various aspects like: - Warming up your data in direct lake datasets - Querying Dynamic Management Views, - Reading Semantic Model meta data to generate documentation - Extend or even forecast your data using Semantic Link By the end of this session, you will not only grasp the basics of Semantic Link but also gain practical insights into its application so you can bring it to practice directly!

As a newcomer, you’re about to dive into the fascinating process of data extraction and report creation. This session will guide you through the essential steps to kickstart your data extraction endeavors.

Microsoft Fabric offers a rich collection of out of the box monitoring functionality. No matter if you are a Fabric administrator, data steward, domain owner, capacity- or solution admin, there is a out of the box solution for your purpose.

The world of data and analytics will never be the same since Microsoft Fabric was publicly announced in 2023. This unified platform served in the Software-as-a-Service model and offering end-to-end analytics capabilities opens up a whole spectrum of completely new ways of working for all personas involved in data analytics, including Power BI developers and users.

Why should we look at reports and search for a specific KPI, if you can just ask your personal assistant for the most recent status of that KPI? Chatting is the new way of interacting with your data in the Era of AI.

Why should we look at reports and search for a specific KPI, if you can just ask your personal assistant for the most recent status of that KPI? Chatting is the new way of interacting with your data in the Era of AI.

Have you always been scheduling your Power BI refresh in the service? Struggling to get your data available in your model in a short amount of time? Then this session is for you! During this session, you will learn advanced patterns to optimally configure and refresh your Power BI data models in the service. We will not only discuss incremental refresh, but also how you can get your data available in your model in (near) real time. We will discuss various patterns from high frequency refresh operations, single table or partition refreshes, detecting data changes in archived partitions and more advanced patters. After this session you will be able to; - Configure incremental refresh. - Refresh individual tables and partitions in a published Power BI model. - Connect your Power BI refresh to other processes and orchestrate centrally end-to-end.

As a newcomer, you’re about to dive into the fascinating process of data extraction and report creation. This session will guide you through the essential steps to kickstart your data extraction endeavors.

Enhance your Fabric Platform development experience by leveraging Continuous Integration and Continuous Deployment (CI/CD), and using the Data Factory Testing Framework to quickly iterate and test your data pipelines. In the first part, we will explore the impact of CI/CD pipelines for Fabric.

Discover the potential of Microsoft Fabric as it connects Power BI and Data Engineering, merging these domains into a unified landscape. This groundbreaking development may ignite your curiosity to explore new frontiers, even though the unfamiliar elements and tasks might seem daunting. Are you unsure about learning new skills such as Python, data integration pipelines, and Spark notebooks? Perhaps the thought of these technologies and coding languages is a bit intimidating because you've never used them before? If you find yourself nodding along, then this session is perfect for you! Join us as we explore Microsoft Fabric from a Power BI perspective. We'll dive into the familiar components we already love, discussing the complete process from ingesting data to serving it to your end users. And the best part? Together, we'll build a solution using the full capabilities of Microsoft Fabric. By the end of this session, you'll feel confident and ready to create your own solutions with Microsoft Fabric. It opens up exciting possibilities to expand your skills and venture into the fascinating world of data from various angles.

Have you been working with Power BI for a while? Then it is time to take the next step with your data modeling skills! You might have heard about Power BI composite models. But in order to implement them successfully, it is important to understand the different storage modes that Power BI has. This is key for a successful implementation and also understand the possible dangers of the storage mode behaviors. More over, let's also talk Calculation groups! You might have been creating numerous calculations in the past for year to date, quarter to date and many other common patterns. With Calculation Groups you can limit the number of redundant measures by creating a common expression parts in a group, that can be applied on top of multiple measures. And finally, we will combine the two together. Dealing with calculation groups in composite models. Cause there are some key-elements you have to keep in mind. Potentially this combination can lead to wrong results, if you do not understand the behavior of the remote and local engine and with that dealing with wholesale and non-wholesale queries. During this demo-rich session I will take you on a tour through all above mentioned features and cases, and explain them one by one. So that after this session you will be able to; - Understand and explain different types of storage modes in Power BI - Combine DirectQuery and import storage modes in a composite model - Successfully implement calculation groups - Understand calculation groups in composite models and their behavior

Have you been working with Power BI for a while? Then it is time to take the next step with your data modeling skills! You might have heard about Power BI composite models. But in order to implement them successfully, it is important to understand the different storage modes that Power BI has. This is key for a successful implementation and also understand the possible dangers of the storage mode behaviors. More over, let's also talk Calculation groups! You might have been creating numerous calculations in the past for year to date, quarter to date and many other common patterns. With Calculation Groups you can limit the number of redundant measures by creating a common expression parts in a group, that can be applied on top of multiple measures. And finally, we will combine the two together. Dealing with calculation groups in composite models. Cause there are some key-elements you have to keep in mind. Potentially this combination can lead to wrong results, if you do not understand the behavior of the remote and local engine and with that dealing with wholesale and non-wholesale queries. During this demo-rich session I will take you on a tour through all above mentioned features and cases, and explain them one by one. So that after this session you will be able to; - Understand and explain different types of storage modes in Power BI - Combine DirectQuery and import storage modes in a composite model - Successfully implement calculation groups - Understand calculation groups in composite models and their behavior

Join us and learn how to build a unified overview on essential information regarding pipelines, performance, data quality, user activity, cost management, and much more. Shared to all stakeholders in a Power BI dataset and report. We'll share our learnings and best-practices along the way in a...

In Power BI we are used to create reports and dashboards really quickly, but in most cases we forget to think about governance, development and maintenance at an enterprise wide scale. During this session I share some best practices about applying DTAP (Development, Test, Acceptance and Production), or better known as multi-tier deployment. During this session I will tell you all about Power BI deployment pipelines and how this helps in setting up Application Lifecycle Management for Power BI content. The session will not only cover deployment pipelines in the Power BI service, but also how you can integrate them with Azure DevOps and bind to other processes. After this session you will be able to: - Understand the importance of Application Lifecycle Management - Know how to setup and use Power BI Deployment Pipelines - Integrate deployment pipelines in Azure DevOps

What is a good data model in Power BI? How do you make sure you have the basics in place so the model you build today, will still work next year? During this session we will lift of from basic level and build up in complexity to understand the key element of a good data model! Everything presented in this session will include real-life examples and can be directly brought to practice. After this session you will be able to: - Explain basic dimensional modeling concepts - Create a well working and performing data model in Power BI - Decide when to use Power Query vs the data model - Decide when to use Calculated columns or measures - Handle complex situations with inactive relationships and role-playing dimensions.

For Power BI solutions, we see it can be challenging to load data from APIs, or to store historical snapshots. This is where you can really benefit from the capabillities of a data platform. Also, if you already have an amazing data platform, we see it can be challenging to load all of its data into a Power BI dataset and still have a performant solution, or to refresh data in near real-time. Finally, daisy-chaining all the steps in the data flow – from source to report/dashboard – can be tricky to design, especially if you don’t want to do a full refresh of the Power BI dataset for performance reasons. The advanced capabilties of both worlds can really complement eachother. Learn how to design and build a truly scalable solution that spans both Power BI and Azure Synapse Analytics. We will help you to get the best out of both worlds, sharing our learnings and best-practices along the way in a demo-rich session! After the session you will be able to: • Better design and implement complex data models, including hybrid tables, aggregations, and combined storage modes (import, DirectQuery, dual). • Orchestrate the end-to-end data processing, with a pipeline chain from data ingest in the data lakehouse to the incremental Power BI dataset refresh. • Use different techniques to identify performance bottlenecks in your solutions and how to solve those ("does it fold"?). • Implement a cost-efficient solution, that still meets the scalability demands.

In this demo-rich session, Dave & Marc discuss the Power BI Cheat Sheet that went viral on social media. The sheet is crammed with Power BI tips and best practices based on years of experience, but with little to no room for comments. No worries! We will describe, demonstrate and discuss all the items! And of course, there will be a Cheat Sheet for everyone attending!