One of the most difficult aspects of providing business-relevant analytics is identifying the business problem.
This blog post is intended for readers who already have some experience on building Data Warehouses with earlier versions of SQL Server (2008 or 2012) and are looking for updating their knowledge on how to do it today on SQL Server 2016 and/or on Azure. In addition, I included some insight on the technology trends on the matter.
Couple days ago The Economist published an article titled "Linux and AWS: Cloud chronicles". The article stressed the undeniable importance of Linux and Amazon Web Services (AWS) in the upsurge of cloud computing and went on to explain how network effects have helped both grow in popularity. Despite similar paths, article predicted that their future will diverge and Linux will "happily plod along" whereas "AWS could end up dominating the IT industry just as IBM’s System/360, a family of mainframe computers, did until the 1980s".
Cloud analytics have changed the world big time. BIGDATAPUMP is an independent and objective solution provider using always the best fit technology for each purpose. Personally I have not written earlier about any specific analytics technology but this time I decided to make an exception. Microsoft and Azure deserve a special focus.
tl;dr: Gartner's Magic Quadrant for advanced analytics doesn't do a good job in helping to decide which analytics tool(s) to acquire. It also misses the point that open source projects are the real leaders in advanced analytics. Vendors that position themselves to complement open source projects rather than competing against them will be ones to watch.