Understanding Microsoft Fabric Costs and Billing

Understanding the cost implications of implementing Microsoft Fabric is crucial for organizations transitioning to this unified analytics platform. This guide explores the fundamentals of Fabric's billing model, helping you make informed decisions about resource allocation and cost management. Microsoft Fabric costs is a difficult topic, apparently, let's make it a bit more understandable!

We will dive deep into the billing mechanism of Microsoft Fabric, unveil Capacity Units and Capacity Unit seconds.

We will look at the implications of the different Fabric Workloads on costs, and learn all about cost optimisation.

After this session you will have a thorough understanding of Fabric cost topics (Capacities, CUs, bursting, smoothing, throttling, cost optimisation, reservations, etc).

Your first Microsoft Fabric Lakehouse implementation

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.

Analysing 4 billion rows of data using Power BI DirectLake and Fabric

Data volumes are going up and up. For most businesses, analysing more data at a faster pace becomes increasingly important to stay ahead.

In this session, I will demonstrate the use of Microsoft Fabric Lakehouses with a massive dataset containing 4 billion rows of data.
We will be looking at the performance implications of analysing such a huge dataset and showcasing Power BI's DirectLake capabilities to handle big data without copying.

After this session you will be able to:
1. Understand DirectLake vs Import & DirectQuery
2. Set up a Power BI DirectLake connection in Microsoft Fabric
3. Run analyses in real-time on datasets in the billions of rows, without breaking a sweat

First public delivery of this presentation, target audience: technical, data engineer, data analyst, preferred duration: 20-30 min

Don't Repeat Yourself, how custom Python modules in Microsoft Fabric give you back hours every day

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.

Aimed at the experienced data engineer, 45-60 minute duration, hands-on

Delta Table Optimisation - Improving Queries using Delta Partitioning and Liquid Clustering

Data volumes are skyrocketing, and with every new project, the pressure is on for data engineers to deliver performant queries over larger and larger 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 optimisations to the test against a massive dataset to demonstrate real-world impacts on speed and efficiency.

We’ll explore the details 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 with Liquid Clustering, an advanced feature that automatically reorganises your data for faster queries. Finally, we’ll show you how to integrate these Delta optimisations into your Microsoft Fabric Lakehouse, so you can power your dashboards, reports, or machine learning pipelines with near real-time insights and without the dreaded performance bottlenecks.

By the end of this session, you will:
1. Understand when, why, and how Delta partitioning improves query performance.
2. Enable 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 optimised 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.

Dashboard are Dead, Talk to your Data!

I have spent the better part of my career designing, refining, and maintaining dashboards in Power BI to help my clients answer business questions. Until now.

In this session, we will break free from predefined visuals and fixed dashboards. It is time to talk to your data!

Over the past years, everybody has been exposed to AI LLMs such as ChatGPT, Claude, and Gemini.
What if I told you that you could chat with your data, just like you can chat with your AI?

With Fabric Data Agents, you can! You get a chat interface that understands the context of your data. You can ask questions in natural language (no need to learn SQL or DAX!) and get answers, fast.

In this session we will start by explaining some of the basics behind the Fabric Data Agents.
Then we'll jump into the prerequisites in order to start using the Data Agents.

After all the boring stuff is done I will take you into a live demo of setting up the Agent, tuning it to get accurate responses, and will show how we can chat with our own data to generate insights and drive decision making.

You'll walk away from this session understanding the following:
- Why this shift from dashboards to chatting matters for you and your organisation
- How Fabric Data Agents actually work "under the hood"
- How to build, configure, and use your first Fabric Data Agent

If you are involved in building modern data platforms, please consider Data Agents as an amazing tool to help users find insights faster!