SQL has opened a lot of doors for me. It's a starting point, not an end though. Learning about dictionaries, lists, and other data structures has proven valuable and compliments SQL and tabular datasets very nicely. I got into those areas by working with SQL generators (ORM's) written in Python (airflow).

My thinking on SQL has evolved and lately I see it as a set definition tool. "Do action X on dataset Y." It's really useful for understanding data structures and data meaning too.

I've worked with more than a few SDE's who look down on SQL, but it's a really good tool when used properly, and it cuts across many technologies. Writing code to write SQL can be very powerful. And sometimes coded or scripted data wrangling without SQL is very useful too.

15 years ago SQL knowledge was not that widespread and it was easy to get tagged as a report writer. Today, a lot more business, product, finance, and accounting people are really strong with SQL, and rely heavily on exporting data to excel for further analysis. Knowing how to answer business questions, get insights out of the data, and define or categorize sets of data are all enhanced by SQL. Report writing is not as much of a thing anymore because people want to view the data in diverse ways.

The barrier to entry is low with SQL, but learning it well takes time and some mistakes to get efficient and precise with it. 15 years later I am still learning new uses for it. One example is JSON querying and transformation which is supported by hive, presto, and some other compute platforms. It's easy to mix and match JSON, arrays, and tabular data structures, in one or more tables, from the same SQL query.