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A data analytics platform has to be able to look at data from a wide variety of systems and produce outputs that can be read by a wide variety of systems.In practice, this means that it needs to be able to ingest and excrete CSV quickly, reliably, repeatably and painlessly.I know it's not scientifically rigorous to do a comparison like this when I don't have equal experience with both databases, but this is not an academic exercise – it's a real-world comparison.I have done my honest best to get my facts about MS SQL Server right – we all know it is impossible to bullshit the whole internet.
Every data analytics platform worth mentioning is Turing complete, which means, give or take, that any one of them can do anything that any other one can do.
Over the years I have discussed the issue of Postgre SQL vs. Unless otherwise stated I am referring to Postgre SQL 9.3 and MS SQL Server 2014, even though my experience with MS SQL Server is with versions 2008 R – for the sake of fairness and relevance I want to compare the latest version of Postgre SQL to the latest version of MS SQL Server.
Where I have made claims about MS SQL Server I have done my best to check that they apply to version 2014 by consulting Microsoft's own documentation – although, for reasons I will get to, I have also had to rely largely on Google, Stack Overflow and the users of the internet.
(This may sound fussy or inconvenient, but it is actually an example of a well-established design principle.
It makes sense: would you rather find out your import went wrong now, or a month from now when your client complains that your results are off? Most people don't believe me when I tell them this. Usually they observe something like: easy to write (I wrote one in C and plumbed it into PHP a year or two ago, because I wasn't happy with its native CSV-handling functions.