Data Vault 2.0 still suffers from an often unfair reputation. Frequently described as “too complex”, “too normalized”, or “not performant enough”, it is sometimes dismissed without being truly understood. Yet, in many complex and evolving environments, it is precisely...
Building a data architecture is rarely a technology problem. It is first and foremost a matter of framing, prioritization, and long-term discipline. Many data platforms fail not because the tools are bad, but because the foundations were designed to address an...
A misconception persists around Data Vault: that it can only be applied to traditional relational databases. As soon as Hadoop, MongoDB, or more broadly NoSQL technologies are mentioned, Data Vault is often considered unsuitable, too relational, or too normalized....
Data Vault modeling and star schema modeling are often presented as opposing approaches, as if one had to be chosen over the other. In reality, this opposition is artificial. These two approaches do not address the same use cases, do not pursue the same objectives,...
Rome was not built in a day, and the same is true for decision-support and data systems. These systems have been built in successive layers as business needs and new projects emerged, often with limited overall consistency. The urgency of new requirements and the...
Data Vault 2.0 is not a universal answer. But in certain contexts, it is no longer just one option among others: it becomes the most suitable framework for securing, industrializing and evolving the data platform over time. Identifying these situations is key to...
