I found that it depends on where your interest lies. If you just want to learn practical SQL then I have historically found the Celko books (https://en.wikiped

I found that it depends on where your interest lies. If you just want to learn practical SQL then I have historically found the Celko books (https://en.wikipedia.org/wiki/Joe_Celko https://en.wikipedia.org/wiki/Joe_Celko) as well as, more recently, the No Starch Press books (https://nostarch.com/practical-sql-2nd-edition https://nostarch.com/practical-sql-2nd-edition) very well written. Everyone learns differently however. I have a colleague who really enjoyed the O’Reilly books specific to SQL from a data analytics perspective. (Apologies, no link for that) You might frequently find the No Starch and O’Reilly books on Humble Bundle, (https://www.humblebundle.com/books?hmb_source=navbar https://www.humblebundle.com/books?hmb_source=navbar) if that is available in your location.There’s often loads of overlap between bundles. I’m sure I’ve bought the python book about 5 times so far but I don’t mind as it’s great value. If you want to learn about database theory, however, as well as the practicalities of SQL, then I found that most of the resources I used when I did this at uni were online. The book we used was the Connolly/Begg book (https://www.pearson.com/us/higher-education/program/Connolly-Database-Systems-A-Practical-Approach-to-Design-Implementation-and-Management-6th-Edition/PGM116956.html?tab=overview https://www.pearson.com/us/higher-education/program/Connolly...) I don’t think this effectively answered the “why” of your question, however. My guess is that SQL is a very well established domain language and when it comes to data normalised across many tuples it’s the standard for manipulation. I don’t think that dataset size is the primary reason to manipulate data via SQL; I think that the moment your programme starts to need more than a single flat file data source, you naturally start to think about normalisation, indexes etc for performance and sanity. If I may, however: I am coming at this from the other way in that I am far more often manipulating CSV and excel data and would like some good resources in how to quickly load that in to a dataframe in pandas or similar vs using SQL. (If I’m responding to a thread hijack I may as well go all in and totally derail it)