In this article, learn how PostgreSQL powers data science workflows, including query execution, performance optimization, indexing, data retrieval, and more.
There’s a part of the data science stack that rarely gets discussed. Not because it’s unimportant, but because it’s already been decided long before you arrive. Somewhere upstream, engineers chose a relational database. In many cases, they chose PostgreSQL. And, since it often becomes a ‘background’ system, it’s very easy to underestimate its impact.
It’s seen as just a place to pull data from before the “real work” begins in Python. It sits beneath notebooks, pipelines, and dashboards, rarely drawing