![]() As an Excel power user, you can probably think of how you might write these questions as formulas if armed with the right dataset. How many web visits did we get in January? What's our payment funnel abandonment rate for the product we just launched? Which sales reps are building more pipeline than others? These are all queries, in "human," not "computer," speak. Even when you pull data from something that doesn't feel technical (think Google Analytics, Stripe, or Salesforce), behind the scenes you're querying a database. The data you're working with in Excel had to come from somewhere. Download our free workbook to learn how to translate go-to Excel functions into SQL. No matter how vigilant you are, no matter how many times you comb through a spreadsheet for typos and broken formulas, you might still miss something.įree workbook: The Excel User's Quick Start Guide to SQLīonus: Harness your Excel knowledge to learn SQL with these quick tips and tricks. Excel's flexibility makes enforcing consistency and accuracy in large datasets nearly impossible. However, if one cell can be manipulated easily, it's harder to trust the integrity of the spreadsheet as a whole. Since each cell is its own entity, you have a lot of freedom to add footnotes, merge cells together, or plot out a needlepoint pattern. ![]() That might sound kind of counter-intuitive-flexibility is one of the reasons people love Excel. ![]() Excel can handle up to a million rows, but when you're working with a large dataset or doing heavy duty analysis-applying formulas to a bunch of cells, linking multiple spreadsheets, or connecting to other workbooks-it slows down way before you hit the row limit.Įxcel has another weakness that can lead to inefficiency: its structure is too flexible. Usually, large files or workbooks full of formulas are to blame. You've probably also worked on a spreadsheet that was painfully slow to edit, where every click was accompanied by 10 seconds of your screen freezing and the spinning wheel of death. ![]()
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