Information changes in intricate ways over time. For example, prices for goods change over time and business plans future pricing like discounts for “Black Friday.” These prices may be saved long before they are valid in real life and therefore in operating systems. A data warehouse with a well-designed bitemporal historization can store this future information about prices. And also enable business users to travel through time to have different views on their data: past, present and future. The speaker will focus in this session on the method and techniques for getting bitemporal data into a Data Vault and afterwards merging timelines of bitemporal Data Vault Satellites to get data out of the Data Warehouse’s core layer. He will show bitemporal basics for a better understanding of loading data as well as the concepts to provide star schema dimensions as non-, uni- or bitemporal objects.
Target Audience: Data Modeler, Data Architects, ETL-Experts
Prerequisites: Data Vault, Data Modeling