>A machine learning algorithm which can be trained using SQL opens a world of possibilities. The model and the data live in the same space. This is as simple as it gets in terms of architecture. Basically, you only need a database which runs SQL.
First paragraph of the conclusion, and this very much fits with the mindset that's been growing in me in the data world over the past few years. Databases are much more powerful than we think, they're not going to go away, only get better, and having the data and the logic in the same space really removes tons of headaches. ML models, transformation of data, generating json for an API can all be done within the database rather than outside scripting language.
Are others seeing this? Are the current tradeoffs just that more people know python vs sql or database specific languages to where moving logic to postgres or snowflake is looked down on?
First paragraph of the conclusion, and this very much fits with the mindset that's been growing in me in the data world over the past few years. Databases are much more powerful than we think, they're not going to go away, only get better, and having the data and the logic in the same space really removes tons of headaches. ML models, transformation of data, generating json for an API can all be done within the database rather than outside scripting language.
Are others seeing this? Are the current tradeoffs just that more people know python vs sql or database specific languages to where moving logic to postgres or snowflake is looked down on?