SQL for Data Analysis

Preface

Over the past 20 years, I’ve spent many of my working hours manipulating data with SQL. For most of those years, I’ve worked in technology companies spanning a wide range of consumer and business-to-business industries. In that time, volumes of data have increased dramatically, and the technology I get to use has improved by leaps and bounds. Databases are faster than ever, and the reporting and visualization tools used to communicate the meaning in the data are more powerful than ever. One thing that has remained remarkably constant, however, is SQL being a key part of my toolbox.

I remember when I first learned SQL. I started my career in finance, where spreadsheets rule, and I’d gotten pretty good at writing formulas and memorizing all those keyboard shortcuts. One day I totally geeked out and Ctrl- and Alt-clicked every key on my keyboard just to see what would happen (and then created a cheat sheet for my peers). That was part fun and part survival: the faster I was with my spreadsheets, the more likely I would be to finish my work before midnight so I could go home and get some sleep. Spreadsheet mastery got me in the door at my next role, a startup where I was first introduced to databases and SQL.

Part of my role involved crunching inventory data in spreadsheets, and thanks to early internet scale, the data sets were sometimes tens of thousands of rows. This was “big data” at the time, at least for me. I got in the habit of going for a cup of coffee or for lunch ...