erwin is best known for its market leading data modelling software but is increasingly being recognised by industry analysts for its data governance capabilities in the form of the erwin Data Intelligence Suite. Indeed, Gartner has put them in the Magic Quadrant for Metadata Management Solutions (Nov. 2020).

Datavault is a great place to build your career in data! How can we justify that claim you may ask? Well, in this blog we will tell you why. Firstly we have a have a great attitude towards learning and prioritise developing new skills within our staff.

This year’s World Wide Data Vault Consortium (WWDVC) conference– held in Vermont – provided the first opportunity for the movers and shakers in the Data Vault world to get together face-to-face in three years.

Data-modeller John Giles’ consultancy work has stretched from working with his home state’s fire service and the Salvation Army to private medical firm BUPA.

Data-modeller John Giles’ consultancy work has stretched from working with his home state’s fire service and the Salvation Army to private medical firm BUPA.

It is still less than two years since Snowflake floated on the New York Stock Exchange, raising $3.4billion – one of the largest flotations by a software company in corporate history. The rapid growth of the American cloud-based data warehousing company has helped create a large tailwind of business opportunities across the industry.

You could be forgiven for thinking life has just become a little complicated if you ever discover you have an elephant in the room… but what happens when you find one in your fridge?

Data Vault 2.0 is a concept for designing Data Warehouses. Invented by data warehouse guru Dan Linstedt it has many technical advantages, but it also has significant business benefits:

Over the past four or five years, demand for data engineers has been growing and growing as more and more businesses realise that data is their most valuable asset, well after their staff of course.

The Data Vault method has been around since the early 2000’s and has evolved and matured into the method of choice for many corporates. Even after such a long time, awareness and understanding are lower than you would expect and there isn’t much information about it on the web.

This blog is the third and final part of the series based on a Keynote talk at this year's World-Wide Data Vault Conference on the role of Data Vault 2.0 in supporting Data Science. The third blog goes through how to apply patterns to Data Science Projects.

This is the second part of a four-part blog series based on a Keynote talk at this year's World-Wide Data Vault Conference on the role of Data Vault 2.0 in supporting Data Science. The second blog explores lessons from a use case in academia.