Like a few others in the community, I will be attending Scalefree’s Data Dreamland conference in Hannover September 23rd and 24th! I will be presenting on optimizing data architecture through a hybrid Data Fabric and Data Mesh approach. If you haven't yet, get your tickets here: https://lnkd.in/ebqPAXjw 🎙️Combining Data Fabric and Data Mesh: Advancing Data Management with Hybrid Architecture 📅 Tuesday, September 24th 🕦 11:30 AM

Here's the link to our webinar where Daniel Olsberg, from areto, and I discuss the importance of prioritizing progress over perfection in your data automation journey. Take a look, especially if you're interested in hearing the checkpoints of launching a Minimum Viable Product (MVP) over a Proof of Concept (POC), and learning how this approach can accelerate business value and efficiency. Take care 👋

Hashing is a crucial part of Data Vault implementations. They help quickly identifying deltas, by not having to compare every single attribute of a satellite, but instead comparing the hashed value over all of these attributes. This helps to reduce the complexity of queries being written, since significantly fewer columns need to be fully specified. But now imagine a fully automated Raw Data Vault implementation, would you still generate Hashkeys and Hashdiffs? Since you don't write the loading scripts of satellites by yourself, what benefit do hash values bring to the Data Vault implementation? Wouldn't it be nicer to directly have business keys everywhere? You could argue that delta detection might be slower, when all columns need to be compared, but does anyone have experience if this is really the case? On modern databases, I would imagine this delta detection to not have an actual impact on overall performance. What's your opinion on skipping hashes? Let me know!

Where are you guys with Microsoft Fabric?

What do you think? Brought to you by Vaultspeed this discussion delves into: Understanding of Kimball and Data Vault Methodologies their purposes, strengths, and weaknesses. How to adapt to Changes in Data Structures, and in particular how Data Vault can efficiently manage changes such as mergers, acquisitions, and expansions which might require rapid integration of new data types into existing models without compromising data integrity or historical data. A demonstration of VaultSpeed to illustrate how automation tools can simplify and accelerate the modelling process, showing how these tools can generate SQL and handle deployments. Practical Implementation and Transition Strategies on how to transition from a Kimball model to a Data Vault model using automation, the discussion provides practical strategies that you can apply directly to their work, especially when looking to enhance the resilience and flexibility of their data architecture. #vaultspeed #datamodelling #kimball #datavault #datawarehouse