Bas Land

Het intern delen van Power BI-rapporten binnen een organisatie is inmiddels een bekende praktijk. Maar wat als er een behoefte ontstaat om rapportages te delen met externe partijen zoals klanten, leveranciers of samenwerkingspartners?

De meeste data professionals worstelen hun hele carrière met tools, titels en technische to-do lijstjes aan. Maar wat als echte groei niet zit in wat je kent, maar in hoe je rolt?

Warning! This session may contain very DRY content! Dont Repeat Yourself, or DRY, is a concept in software engineering that governs the way software is written by stating that you should never repeat yourself. As a data engineer working with Microsoft Fabric, when you start building a data lakehouse you will be writing a lot of code to connect to source systems, copy and transform data, and orchestrate your ELT process. Fabric allows you to write custom Python modules that can be called from within your notebooks, in order to streamline these processes. Never again you'll have to write the same function twice again! In this very practical session we will dive deep into: 1. Creating a very simple Python module using Visual Studio Code 2. Publishing our Python module to Microsoft Fabric 3. Calling functions in Python from Fabric notebooks After this session you will go home never having to repeat yourself again, because you will be writing reusable Python modules for all your data engineering needs.

Data volumes are skyrocketing, and with every new project, the pressure is on for data engineers to deliver snappy queries over ever-growing datasets. In this session, we will deep dive into how Delta Lake’s partitioning and Liquid Clustering capabilities can transform query performance in Microsoft Fabric. We’ll be putting these optimizations to the test against a massive dataset—millions (or even billions!) of rows—to demonstrate real-world impacts on speed and efficiency. We’ll explore the nuts and bolts of Delta partitioning to ensure your data is stored in the most optimal way, reducing query overhead and slashing runtimes. Then we’ll crank it up a notch with Liquid Clustering, an advanced feature that automatically reorganizes your data for blazing-fast analytics. Finally, we’ll show you how to integrate these Delta optimizations into your Microsoft Fabric Lakehouse, so you can power your dashboards, reports, or machine learning pipelines with near real-time insights—without the dreaded performance bottlenecks. By the end of this session, you will: 1. Understand how and why Delta partitioning supercharges query performance. 2. Harness Liquid Clustering in Delta Lake to keep your data lean, mean, and query-ready. 3. Integrate partitioned and clustered Delta tables seamlessly with Microsoft Fabric for next-level analytics. Brace yourself: This session may contain dangerously optimized partition strategies and an overdose of high-speed query demos! If you’re a data engineer looking for hands-on techniques to crush query latencies and boost productivity in Microsoft Fabric, then this is your must-attend deep dive. Get ready to leave your old, slow queries in the dust.

Level up your Power BI modelling skills with this practical session. While setting up a Power BI Semantic Model can easily be done using Power BI Desktop, some of the tasks can feel like a tedious grind.

In this session, Bas, a seasoned data engineer with over a decade of expertise in SQL Server, Azure SQL, and now Fabric, shares his transition journey and insights from building data warehouses with SQL to building them with lakehouses in Microsoft Fabric. In this session you will learn enough about Fabric and lakehouse concepts to start building your own solutions right away. The session starts out with a few theoretical concepts to give guidelines, and is then filled with practical examples. We will go through everything necessary for your first project. From setting up the Fabric Capacity to Workspaces management, environments and importing your own custom Python code. We end with a practical case study where we implement a Fabric Lakehouse solution for a small B2B services company. During this part you will see examples of the Data Factory orchestration pipelines, the folder and file structure of the data lake, and the PySpark notebooks you need to transform your raw data into insightful information.