22-25 April 2026

Nico Jacobs

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 Jacobs's Sessions

Fabric Data AgentsSQLBits 2026

Fabric Data Agents allow you to query your data using natural language. Based on the question asked, an LLM constructs the necessary SQL, KQL or DAX queries and interprets the data returned.

The Power of Paginated ReportsSQLBits 2026

Power BI reports lack options for advanced small multiples. Creating printable reports can also be challenging: Ever tried to scroll a scrollbar in a PDF? See how Power BI paginated reports can solve these challenges!

What are Fabric Materialized Lake ViewsSQLBits 2026

With Fabric Materialized Lake Views you can setup your data ingestion and transformations using a familiar SQL syntax. This demo rich session shows how to get started with

When Translytical Task Flow meets Fabric Data FunctionSQLBits 2026

See Translytical Task Flow in action: It combines Fabric User Data Functions (Python functions that can amongst others access and modify data) with Power BI actions to call them, and Power BI visuals that provide input to them.

It's about time: Calendar-based time intelligence in Power BISQLBits 2026

Power BI introduces a calendar-aware engine that lets you define multiple calendar structures directly in your model, opening doors to cleaner, more accurate time calculations without heroic hacks.

Getting started with SparkML in Microsoft FabricSQLBits 2024

Microsoft Fabric supports machine learning in their Spark notebooks. See an end-to-end demo of how Fabric data is used to build machine learning models upon, which are then used to make predictions about other Fabric datasets.