Welcome to Your Analytical Bridge, the place where data and business insights come together.
Here, I share stories and experiences from my work with technology, and what I learn from it. Hopefully, it will inspire, help, or just bring a smile to your face!
Let's build a bridge together to a better understanding of Data, Fabric, and AI.
You can find out more about me here.
Welkom bij Your Analytical Bridge, de plek waar data en inzicht voor de business samen komen.
Op deze plek deel ik verhalen en ervaringen uit mijn werk met de techniek, en wat ik daarvan leer. Hopelijk inspireert het, helpt het, of brengt het gewoon een glimlach!"
Laten we samen een brug slaan naar een beter begrip van Data, Fabric en AI.
In my last blog I wrote about the pattern of a core data flow which evolutes from the BI core definition from the 90’s. We also have seen a tradition BI example on SQL server with STG, DWH and DM concepts.
Oke, I'll be honest:
In the previous blog, we explored my data journey to answer one key question: What is BI?Now, let’s continue that journey and see how this connects to broader data patterns and data flows.
So, like everybody I sat down to put Rayfin to the test. The plan? Make an app for our metadata file, simple. Spoiler: it worked. Eventually.
Your eyes are still trying to catch up, but you head for the car or train anyway. There you are, surrounded by fellow commuters, with one familiar question crossing your mind: why am I doing this, anyway?
FabCon Atlanta 2026 was overwhelmed with tons of new announcements around SQL and Fabric. A lot of sexy ones! Some very standard and logic. This week, I tested a small new preview feature, which actually was a giant step forward in my opinion!
A few weeks ago, I reposted my colleague’s notebook that shows how to deploy a Data Agent in just 15 minutes.
It’s time for another Fabric adventure with Wortell – Smart. And yes… time for snaecks! 😄
A few months ago, I wrote a blog about pulling data directly from a SharePoint list instead of relying on a OneLake Sync.
Last week, I found myself wrestling with an issue for my client. The mission? Rebuild a Spark SQL query to create a finance table for reporting their organizational formation. Sounds straightforward, right?
