Excited to be presenting alongside @Christof Wenzeritt at Data Dreamland! Main topic is Data Mesh and how to do one. It offers an effective approach for managing large-scale data, but the transition isn't always smooth sailing. Join us when we delve into the challenges of designing ownership structures within a Data Mesh framework. We'll explore how to achieve the optimal balance between technical expertise and organizational design. Let's share our knowledge and navigate the exciting world of Data Mesh together! Get your ticket here: https://scalefr.ee/d7goui

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At some point in each Data Vault project, the source data is analyzed, and a Data Vault 2.0 model is drafted. I saw a lot of different locations where teams draft their Data Vault model, Excel sheets, drawing tools like Draw.io or Miro.com, dedicated modeling tools, directly inside their automation tools, or on pen and paper. My past experience highlighted three most important things to consider when deciding where to model: - Must fit into the tool stack: if your automation tool offers Data Vault modeling, do it in there - Ease of use: Simplicity is important to streamline initial modeling, changes and additions - Persistent and centralized: All components of the whole model should be stored in a central place, to help other modelers identify what has been done already. Where are you currently designing the Data Vault model? What are your experiences? Let me know!

This fascinating talk points out that with every technological leap forward humans naturally try and use it for short term gain at the expense of the long term value to be had in using that technology. What are your thoughts on this thorny issue especially as applies to building data models?

Do you have any rituals or routines on Sundays to wrap up the weekend and prepare for the week ahead? I like to get a quick overview of the next day to start the week strong. What’s your go-to routine?