[tilt_error]

The premise for this article is wrong!

The author describes using SQL to pull facts from history; who was the number one customer the last week, who abandoned online orders and so on.

The premise should instead be how to fit a model onto your business data so that you better can guess who will be the number one customer next week, what (s)he will order and so on.

The problem that ML addresses is how to arrive at that model, under the assumption that you can use historic data to pick either model or parameterise a model.

SQL has it merits, as does the relational database model, but this has nothing to do with creating models (even though we are modelling the data itself). The author gives some examples that are, frankly, trivial.

But he has a good argument around namedropping "hot" technology when your business need does not incorporate distributed trust (blockchain), modelling behaviour (or some such) using ML and so on.