Bas Land
That Fabric Guy - Data Architect - Co-Founder
Woudenberg, The Netherlands
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Bas is co-founder of two data-related companies. He works as a data architect and specialises in Microsoft Fabric.
He speaks and blogs about these experiences to share knowledge with the community.
He is an experienced data engineer and architect with over 10 years of experience in Microsoft SQL Server, Azure and now Fabric technology implementations.
In his spare time he likes to practice sports (Brazilian jiu-jitsu, running, weight lifting) and also traveling with his wife and their two-year-old dachshund (daxhund?) Chester.
Area of Expertise
Topics
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
Using Tabular Editor and best practices to speed up Power BI development - a live demo
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. Are you sick of clicking 4 buttons to change the settings of 1 measure, only to do it 20 more times for all the other measures in your model?
Join me for a live demonstration of how I use Tabular Editor in order to massively speed up this process.
The combination of adhering to best practices and automation using Tabular Editor will greatly improve both the quality of work and the speed in which I can deliver models.
After attending this session, people will have a working understanding and examples to start practicing on their own projects right away. We will learn:
1. Best practices for data ingestion: The most efficient ways to get data into Power BI
2. Mastering naming conventions: not only will they make your model easier to understand, naming conventions are also key to automations
3. Bulk measure creation and editting: save time and hundreds of mouse clicks!
4. Automatic model cleaning: using code to automatically hide columns, set data types and more!
5. Best practice analyzer: use this free tool to further improve your models.
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