Thanks to @Dan Linstedt for this great overview video. This was the inspiration for this new page on perplexity.ai that provides a deep dive into Data Vault. Check it out here https://www.perplexity.ai/page/the-power-of-data-vault-d4CxnNfmRRmAJOwfGxm1Fw Love to hear your comments on how well or otherwise LLM's are able to collate and interpret presentations like this one. FY this used the new Llama 405 billion parameter model to do its thing. Thanks to Wherescape for sponsoring this post. @Melissa Zuro

Hello everyone. I've one question, what accompanied my whole DV journey. Maybe I didn't clearly understand but I live under the belief that the test, when you must be able to reconstruct every data extract from RDV layer, is called Jedi test. Is it correct? And WHY? Why is the test called as it is?

What are the pro's and con's in your experience? 📊 **Normalized Data Modeling** ⭐ **Star Schema** 🔗 **Data Vault** Michael Kahn breaks down the differences in these 3 approaches. His channel has racked up over 4m views and he is in the Top 5 online educators for data pipeline engineering for small teams to deliver better analytics, faster. 👉 [Watch and Comment Now!] #DataModeling #DataEngineering #BigData #Analytics #TechTalk

Do you agree or disagree with Perplexity.ai 's analysis? 1. Ownership and Structure Data Mesh is fundamentally about decentralising data ownership and treating data as a product. In this approach, data is managed by cross-functional, domain-specific teams, each responsible for their own data products. This decentralization aims to eliminate bottlenecks and empower teams to innovate independently. On the other hand, Data Vault is a centralised data modelling methodology designed for building scalable and flexible data warehouses. It organizes data into three core components: Hubs, Links, and Satellites, which help maintain historical tracking and ensure data integrity. 2. Use Cases and Flexibility Data Mesh is best suited for large-scale, domain-diverse organisations that need to democratise data ownership and processing. It promotes agility and adaptability by allowing each domain to manage its data independently, adhering to company-wide standards. Conversely, Data Vault excels in environments with complex, evolving data landscapes. Its modular design allows for easy integration of new systems and changes, making it ideal for companies that require a robust, adaptable data warehousing solution. 3. Governance and Complexity Governance in Data Mesh is federated, meaning that while each domain has autonomy, there are overarching standards to ensure consistency and compliance. This approach can introduce organizational and cultural complexity due to the shift towards decentralization. In contrast, Data Vault focuses on technical governance through its standardized modeling techniques, ensuring data lineage, auditability, and resilience. While Data Vault can be technically complex due to its specific modeling requirements, it provides a structured approach to managing data integration and historical tracking. Agree or disagree? Let us know your point of view Citations: [1] https://atlan.com/data-mesh-vs-data-vault/