In principal Data Mesh offers a brilliant approach to managing data at scale, decentralizing ownership while maintaining centralized governance. However, that requires a lot of change in the organization and without a clear strategy, Data Mesh can easily lead to anarchy, data silos and many more meetings. I'm very much looking forward to be taking the stage with @Marc Winkelmann at Data Dreamland to dive into this topic with our presentation: "Data Mesh Governance: Taming the Wild West of Distributed Ownership" I hope to see many of you in Hanover! Sign up here if you want to join: https://scalefr.ee/d7goui #DataGovernance #DataManagement #DataDreamland

Relational stages handle structured data with real-time processing and schema validation, while Data Lakes are built for unstructured data, offering flexibility and scalability for large datasets and analytics. Where do you see the biggest differences in how they’re used in your Data Vault setup?

The question arose as to the correct way to model address data. Michael Olschimke explained his point of view and shared his idea on how to proceed in the latest session of Data Vault Friday. You can find the video either here in the classroom or below. How would you model address data? Would you do it differently to Michael?

Klarna is planning to halve its workforce, leveraging AI to boost productivity ahead of a potential IPO. They've already trimmed from 5,000 to 3,800 employees, with more cuts likely. **Question for you:** What do you think of this from an ethical perspective? Would you let AI run your team? 🤔

Hey everyone, I'm curious to know—what was your entry point into the world of data? For me, it all began with Fantasy Football. I wanted to create my own stat analytics, so I started querying player databases using an API. There’s so much to analyze in American Football, and with our fantasy draft kicking off this Sunday, I’m reminded of those early days. So, what was your first data project? How did you get started? Looking forward to hearing your stories!