SQL can now replace Python for most supervised ML tasks. Should you make the switch?
When it comes to machine learning, I’m an avid fan of attacking data where it lives. 90%+ of the time, that’s going to be a relational database, assuming we’re talking about supervised machine learning.
Python is amazing, but pulling dozens of GB of data whenever you want to train a model is a huge bottleneck, especially if you need to retrain them frequently. Eliminating data movement makes a lot of sense. SQL is your friend.
For this article, I’ll use an always-free Oracle Database 21c provisioned on Oracle Cloud. I’m not sure if you can translate the logic to other database vendors. Oracle works like a charm, and the database you provision won’t cost you a dime — ever.
I’ll leave the Python vs. Oracle for machine learning on huge dataset comparison for some other time. Today, it’s all about getting back to basics.
I’ll use the following dataset today:
- Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems. University of California, Irvine, School of Information and Computer Sciences. Retrieved…
