Give an analyst AWS Athena, DuckDB, Snowflake, whatever, and they won't have to worry about looking up what m6.xlarge is and how it's different from c6g.large.
Give an analyst AWS Athena, DuckDB, Snowflake, whatever, and they won't have to worry about looking up what m6.xlarge is and how it's different from c6g.large.
Especially when considering testability and composability, using a DataFrame API inside regular languages like Python is far superior IMO.
Sure, Python code is more testable and composable (and I do love that). Have I seen _any_ analysts write tests or compose their queries? I'm not saying these people don't exist, but I have yet to bump into any.
But coming into such a discussion dunking on a tool cuz it’s not for the masses makes no sense.
So I'm very much advocating for people to "[u]se whatever tools work best".
(That is - now I'm doing this. In the past I taught a course on pandas data analytics and spoke at a few PyData conferences and meetups, partly about dataframes and how useful they are. So I'm very much guilty of what all of the above.)