Many posts here are focused on classic ETL. I'm working on a small project for handling data just after ETL. It’s for dealing with annoyingly large data, bigger than RAM but sitting on a personal PC. It basically…
Thanks, this is helpful and informative.
Great and honest points. >Secondly, for some reason data science just doesn't excite me as much as typical software development goes Fair enough. Part of the reason is "data science" has been so jammed pack of nonsense…
You have a great question, but the answers given exhibit the wrong attitude. The short answer is that the recruiter likely didn't know what they were doing and that you belong in a principal or management role, not in…
What does ballpit mean in this context? By your definition, what does it mean to say Google has a "ballpit"? If I wanted to determine technical competency in addition to a verbal interview, why wouldn't I use a…
Short answer: 1. Don't do this if you have no reasonable pipeline to build the ML/AI product. 2. However, what you're describing is standard, and maybe even virtuous for people in startups. There is an outside chance…
Many posts here are focused on classic ETL. I'm working on a small project for handling data just after ETL. It’s for dealing with annoyingly large data, bigger than RAM but sitting on a personal PC. It basically…
Thanks, this is helpful and informative.
Great and honest points. >Secondly, for some reason data science just doesn't excite me as much as typical software development goes Fair enough. Part of the reason is "data science" has been so jammed pack of nonsense…
You have a great question, but the answers given exhibit the wrong attitude. The short answer is that the recruiter likely didn't know what they were doing and that you belong in a principal or management role, not in…
What does ballpit mean in this context? By your definition, what does it mean to say Google has a "ballpit"? If I wanted to determine technical competency in addition to a verbal interview, why wouldn't I use a…
Short answer: 1. Don't do this if you have no reasonable pipeline to build the ML/AI product. 2. However, what you're describing is standard, and maybe even virtuous for people in startups. There is an outside chance…