Since its first incarnation almost 35 years ago in my IBM Systems Journal article, the data warehouse (DW) has remained a key architectural pattern for decision-making support. By decision-making support, I mean everything from simple reporting and querying to AI-based predictive analytics. Of course, the first DW architecture was designed for queries and reports. A variety of additional concepts of varying breadth and quality — such as data mining, the logical data warehouse, and data lakes — have expanded the scope of the original DW thinking or have sought to displace it entirely. None have succeeded in killing off the DW. Now, over the past couple of years, three new adversaries have emerged: data fabric, data mesh, and the data lakehouse.
Will any or all of them kill off the data warehouse? It’s a fun question, but the wrong one! A better question — and one with a more useful answer — is: how can they complement DW thinking, and how could they improve decision-making support in an era of rapidly expanding digital business? We’ll look at each of these new approaches in this series. In this post, we’ll focus on data fabric.
Read more in https://www.datavirtualizationblog.com. Originally published on October 21, 2021
