The shocking truth is that Chief Information Officers (CIOs) are being convinced that hand-coding their Data Vault solutions is a good idea. This is a mistake. There are two groups who convince the CIO to make this expensive mistake. I have seen examples of both in recent weeks.

Starting with Data Vault data modeling can be a daunting task, and it's easy to fall into several common pitfalls. Here, we’ll explore five of the most frequent mistakes that can lead to problems in Data Vault projects and provide insights into how to avoid them.

Successful businesses are always looking for ways to streamline and optimize their data workflows. The combination of AutomateDV, Data Vault, and dbt Cloud offers a powerful solution for data teams aiming to build a robust Data Mesh.

Whether you are an experienced data professional or completely new to the industry, understanding Data Vault and its core concepts could be really helpful to your organisation. In this article, we will explore the core principles of Data Vault, exploring 13 terms that every data professional needs to know.

Large Language Models (LLMs) like ChatGPT have introduced unique opportunities for almost every industry. It is like having an all-knowing colleague sit at your desk with you, ready to respond to your queries instantly.

In the dynamic landscape of data management, the search for agility, scalability, and efficiency has led organizations to explore new, innovative approaches. One such innovation gaining traction is the Data Mesh framework. The Data Mesh approach distributes data ownership and decentralizes data architecture, paving the way for enhanced agility and scalability.

Staying ahead of your competition is not just an advantage—it's a necessity. For Business Intelligence managers, data engineers, and IT executives, understanding and adopting Data Vault can be a crucial part of doing that.

In the world of data and business, your choice of a data warehousing methodology can significantly impact your organisation’s ability to harness data for strategic decision-making.

In this blog, we will delve into data contracts, focusing on the implementation strategies, best practices, and their crucial role in navigating a highly distributed and agile data ecosystem, specifically Data Vault.

What are the “hard problems” that your team is dealing with? First, you may ask “what are hard problems?”. Scott Ambler – agile data coach and consulting methodologist – outlined exactly what he means.

In the constantly evolving landscape of data management, Data Vault has emerged as the platform of choice for large corporations worldwide. However, its impact extends far beyond IT departments.

Testing is vital to any successful Data Vault 2.0 project. It helps you build robust and trustworthy data solutions. In a recent Data Vault User Group meet-up, our Data Engineer, Chris Fisher, spoke on the role of testing in a Data Vault project.