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It took a while, but he got there (emphasis mine): >This is an advantage for MS SQL Server whenever you're running a query which is CPU-bound and not IO-bound. In real-life data analytics this happens approximately once every three blue moons. On those very rare, very specific occasions when CPU power is truly the bottleneck, you almost certainly should be using something other than an RDBMS. RDBMSes are not for number crunching. As a data analyst, the tools to be comparing shouldn't be RDBMSs. >As I said in the banner and the intro, I am comparing these databases from the point of view of a data analyst, because I'm a data analyst and I use them for data analysis. I know about SSRS, SSAS, in-memory column stores and so on, but I haven't mentioned them because I don't use them (or equivalent features). Yes, this means this is not a comprehensive comparison of the two databases, and I never said it would be. It also means that if you care mostly about OLTP or data warehousing, you might not find this document very helpful. As for this part, data warehousing, OLAP services, and reporting services (lower case on purpose here) are a very large sub-domain within data analytics. I am not saying that these are everything in analytics, but especially from an enterprise standpoint, these make up the bulk of it. From a tooling and full-stack standpoint, Microsoft is quite strong in this segment. |