In the 3rdpost in this series, we looked at how we prepare data for use with a concept called the Business Data Vault. Now, in this final part, I will show you the basics of how we project the Business Vault and Raw DV tables into star schemas which form the basis for our Information Marts.Raw Data Mart vs. Information MartsAs of Data Vault 2.0, the terminology changed a bit to be more precise. With DV 2.0 we now speak of “
In my last post , we looked at the basics of modeling your data warehouse using the Data Vault 2.0 technique. This time, we get into the details of how we prepare the DV tables for business user access.What is a Business Data Vault?If you have done any investigation into Data Vault on various blogs or the LinkedIn discussion group, you have seen a few terms used that often cause confusion. These terms include:
In my last post , we looked at the need for an Agile Data Engineering solution, issues with some of the current data warehouse modeling approaches, the history of data modeling in general, and Data Vault specifically. This time we get into the technical details of what the Data Vault Model looks like and how you build one.For my examples I will be using a simplyHuman Resources (HR)type model that most people should relate to (even if you have never worked with an HR model). In this post I will walk through how you get from the
The world is changing.No –the world as we knew it in IThaschanged.Big Data & Agile are hot topics.But companies still need tocollect,report, andanalyzetheir data. Usually this requires some form of data warehousing or business intelligence system. So how do we do that in the modern IT landscape in a way that allows us to beagileand either deal directly or indirectly with unstructured and semi structured data?First off, we need to change our evil ways – we can no longer afford to take years to deliver data to the business. We cannot spend months doing detailed analysis to develop use cases and detailed specification documents. Then spend months building enterprise-scale data models only to deploy them and find out the source systems changed and the models have no place to hold the now-relevant data critical to business success.