Nikola Ilic

Direct Lake promises fast reports without data duplication, but many teams see unexpected fallbacks and slow queries. The root cause often isn't the semantic model - it's how the lakehouse tables were designed.

**This tutorial is booked out** Teams adopting Fabric often struggle to move from prototypes to production-ready lakehouses. In this hands-on workshop, you’ll build a complete lakehouse end-to-end: from raw data ingestion through medallion transformation layers to a consumption-ready Direct Lake semantic model.

Direct Lake promises fast reports without data duplication, but many teams see unexpected fallbacks and slow queries. The root cause often isn’t the semantic model – it’s how the lakehouse tables were designed.

For hundreds of years, alchemists tried to figure out how to turn ordinary metals into gold. The modern version of alchemists, let's call them Data Alchemists, try to turn raw, messy data into a high-quality, business-valuable asset.

The session shows an analyst how to use notebooks to perform the same actions as Dataflow Gen2. Looking at 4 areas: source, transformation, cleansing, and destination, examples include append/merge query to dataframe joins, text replace functionality, split and concatenate columns, as well as pivot/unpivot, data type changes, and removing duplicates.

You've heard about the Direct Lake mode in Microsoft Fabric, and, as a Power BI professional, you are curious to learn what all the fuss is about and how you can leverage this groundbreaking feature in real-life scenarios. How does Direct Lake work under the hood?

Have you ever wondered what makes Power BI so fast and powerful when it comes to performance? So powerful, that it performs complex calculations over millions of rows in a blink of an eye.

“What should I do now?” That’s the most common question we’ve been hearing from Power BI professionals since Microsoft Fabric was introduced as an “end-to-end analytics platform” by Microsoft in November 2023. And, although most of the “traditional” Power BI workloads stay the same, there are some new features and…

Ready to elevate your career in data engineering? This workshop is your comprehensive guide to mastering the skills required to succeed as a Microsoft Fabric Data Engineer and ace the DP-700 certification exam.

Have you ever dreamed of becoming an Analytics Engineer and mastering the most in-demand skills in today’s data analytics ecosystem? This course will boost your career opportunities in the analytics realm, while at the same time setting you ready for acquiring the popular DP-600 certificate (Microsoft Fabric Analytics Engineer).

Are you a seasoned T-SQL developer, used to solving each and every challenge by writing plain old SQL? But, now you need to leverage data coming from semi-structured or unstructured sources?

Are you a seasoned T-SQL developer, used to solving each and every challenge by writing plain old SQL? But, now you need to leverage data coming from semi-structured or unstructured sources? What if I tell you that you can get your mission accomplished by writing your favorite T-SQL syntax? In this session, you will learn what is a Warehouse within Microsoft Fabric, how it works behind the scenes, how it compares to a traditional data warehouse, and how you can preserve your "T-SQL Ninja" status even when dealing with the data coming from CSV, Parquet, or Delta files. You'll also understand the difference between the Warehouse and Lakehouse in Microsoft Fabric, and how to leverage T-SQL language to get the best of both worlds, by combining the data from Warehouse and Lakehouse! This session WILL NOT teach you how to write SQL - I assume you are already familiar with the T-SQL basics, and that you are now eager to apply your T-SQL skills in a different environment!

In this session, featuring four speakers from the data platform community, each speaker unfolds their unique journey, revealing personal and professional milestones within the community. Their stories highlight the interconnectedness within the community, emphasizing how mentorship fosters growth and collaboration.

What should you do when DirectQuery is the ONLY available option? How to reduce the data model size, while at the same time satisfying business requirements? I'll share tips, tricks and workarounds learned and implemented in real-life scenarios on enterprise data models.

Congratulations! You've built your shiny fancy Power BI report and your charts look terrific. But, your data refresh takes forever...Or, your report is utterly slow when interacting with visuals...You switch to DirectQuery mode to make things better, just to discover that the report performance sucks even more... And, if you feel lost, I don't blame you - I've also been there! That's why this session is for you: we will examine potential bottlenecks in your Power BI report - starting with defining the best practices for data model size optimization, understanding the importance of query folding feature, all the way to identifying culprits for your slow report rendering. We will wrap it up with learning when and why (not) to use DirectQuery mode, and how to apply improvements on the data source side to make things faster in the Power BI. And, I'll tell you a little secret: there are no slides in this session - from the beginning to the very end, we'll be only in Power BI Desktop!