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She previously covered politics for CBSNews. CNET. Stephanie graduated with a B. A. in communication from Stanford University. Postgre. SQL vs. MS SQL Server. Oops, spoiler alert. This section is a comparison of the two databases in terms of features relevant to data analytics. CSV support. CSV is the de facto standard way of moving structured i. All RDBMSes can dump data into proprietary formats that nothing else can read, which is fine for backups, replication and the like, but no use at all for migrating data from system X to system Y. 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. Lets not understate this a data analytics platform which cannot handle CSV robustly is a broken, useless liability. Postgre. SQLs CSV support is top notch. The COPY TO and COPY FROM commands support the spec outlined in RFC4. CSV standard as well as a multitude of common and not so common variants and dialects. These commands are fast and robust. When an error occurs, they give helpful error messages. Importantly, they will not silently corrupt, misunderstand or alter data. If Postgre. SQL says your import worked, then it worked properly. The slightest whiff of a problem and it abandons the import and throws a helpful error message. 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 offMS SQL Server can neither import nor export CSV. Most people dont believe me when I tell them this. Then, at some point, they see for themselves. Usually they observe something like MS SQL Server silently truncating a text field. MS SQL Servers text encoding handling going wrong. MS SQL Server throwing an error message because it doesnt understand quoting or escaping contrary to popular belief, quoting and escaping are not exotic extensions to CSV. They are fundamental concepts in literally every human readable data serialisation specification. Dont trust anyone who doesnt know what these things areMS SQL Server exporting broken, useless CSVMicrosofts horrendous documentation. How did they manage to overcomplicate something as simple as CSV This is especially baffling because CSV parsers are trivially easy to write I wrote one in C and plumbed it into PHP a year or two ago, because I wasnt happy with its native CSV handling functions. The whole thing took perhaps 1. SWIG, which was new to me at the time. If you dont believe me, download this correctly formatted, standards compliant UTF 8 CSV file and use MS SQL Server to calculate the average string length i. Go on, try it. The answer youre looking for is exactly 1. Naturally, determining this is trivially easy in Postgre. SQL in fact, the most time consuming bit is creating a table with 5. Poor understanding of CSV seems to be endemic at Microsoft that file will break Access and Excel too. Sad but true some database programmers I know recently spent a lot of time and effort writing Python code which sanitises CSV in order to allow MS SQL Server to import it. They werent able to avoid changing the actual data in this process, though. This is as crazy as spending a fortune on Photoshop and then having to write some custom code to get it to open a JPEG, only to find that the image has been altered slightly. Ergonomics. 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. There is no such thing as you can do X in software A but you cant do X in software B. You can do anything in anything all that varies is how hard it is. Good tools make the things you need to do easy poor tools make them hard. Thats what it always boils down to. This is all conceptually true, if not literally true for example, no RDBMS I know of can render 3. D graphics. But any one of them can emulate any calculation a GPU can perform. Postgre. SQL is clearly written by people who actually care about getting stuff done. MS SQL Server feels like it was written by people who never have to actually use MS SQL Server to achieve anything. Here are a few examples to back this up Postgre. SQL supports DROP TABLE IF EXISTS, which is the smart and obvious way of saying if this table doesnt exist, do nothing, but if it does, get rid of it. Something like this DROP TABLE IF EXISTS mytable Heres how you have to do it in MS SQL Server IF OBJECTID Ndbo. NU IS NOT NULL. DROP TABLE dbo. Yes, its only one extra line of code, but notice the mysterious second parameter to the OBJECTID function. You need to replace that with NV to drop a view. Its NP for a stored procedure. I havent learned all the different letters for all the different types of database objects why should I have to Notice also that the table name is repeated unnecessarily. If your concentration slips for a moment, its dead easy to do this IF OBJECTID Ndbo. NU IS NOT NULL. DROP TABLE dbo. See whats happened there This is a reliable source of annoying, time wasting errors. Postgre. SQL supports DROP SCHEMA CASCADE, which drops a schema and all the database objects inside it. This is very, very important for a robust analytics delivery methodology, where tear down and rebuild is the underlying principle of repeatable, auditable, collaborative analytics work. There is no such facility in MS SQL Server. You have to drop all the objects in the schema manually, and in the right order, because if you try to drop an object on which another object depends, MS SQL Server simply throws an error. This gives an idea of how cumbersome this process can be. Postgre. SQL supports CREATE TABLE AS. A wee example CREATE TABLE goodfilms AS. This means you can highlight everything but the first line and execute it, which is a useful and common task when developing SQL code. In MS SQL Server, table creation goes like this instead SELECT. So, to execute the plain SELECT statement, you have to comment out or remove the INTO bit. Yes, commenting out two lines is easy thats not the point. The point is that in Postgre. SQL you can perform this simple task without modifying the code and in MS SQL Server you cant, and that introduces another potential source of bugs and annoyances. In Postgre. SQL, you can execute as many SQL statements as you like in one batch as long as youve ended each statement with a semicolon, you can execute whatever combination of statements you like. For executing automated batch processes or repeatable data builds or output tasks, this is critically important functionality. In MS SQL Server, a CREATE PROCEDURE statement cannot appear halfway through a batch of SQL statements. Theres no good reason for this, its just an arbitrary limitation. It means that extra manual steps are often required to execute a large batch of SQL. Manual steps increase risk and reduce efficiency. Postgre. SQL supports the RETURNING clause, allowing UPDATE, INSERT and DELETE statements to return values from affected rows. This is elegant and useful. Os X Show Hidden Files Dialog'>Os X Show Hidden Files Dialog. MS SQL Server has the OUTPUT clause, which requires a separate table variable definition to function. This is clunky and inconvenient and forces a programmer to create and maintain unnecessary boilerplate code.