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by mbreese
4805 days ago
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One of the big problems with science is the proliferation of Excel "databases". It is very easy to look at a lot of numbers and make quick calculations with them. However, when you start getting into extremely large datasets, your propensity to make mistakes increases. A2:A10 here, B3:B11 there, etc... This is one reason why recent versions of Excel warn you when your formulas aren't in sync. However, all of this is fixed when you are using SQL to properly query a database. Why? Because you are forced to write a SQL statement that details exactly what you want done. With Excel it can all be hidden away behind the cells. With SQL, it's out in front, so it's easier to check. People like to use Excel because they can get an answer quickly without all that "programming". The problems start to arise when you need better tools, but only know Excel. So, in this case, it's not a matter of a craftsman blaming their tools, it's closer to an amateur trying to pretend to be a professional. Excel is a wonderful spreadsheet. It is a horrible database. |
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I mean Excel is at its heart a query language. So your logic equally applies to Excel, why not write a massive query in Excel that does all the calculations in one go so you can see the inner workings?
All you're really doing is playing musical chairs with the data. SQL query language for Excel query language, and data moved from tables to worksheets.
As I said earlier, unless there are procedural changes upstream nothing will change. A tool is just a tool. You can use it in a way to minimise mistakes, or not. Humans are the weak point.
Now there are tools that automatically identify common mistakes but neither Excel or SQL/relational database engines are in that class.