Most of the comments in this thread are focusing on the author calling ETL boring -- that is the title after all. But I found the greater point of the article t

Most of the comments in this thread are focusing on the author calling ETL boring -- that is the title after all. But I found the greater point of the article to be about empowering data scientists and giving them autonomy. This post reminds me of Jerry Chen's DDI post [1], except it's about data science. The notion that a data scientist's only job is to "write a statistical model" and then it's someone else's problem to run it in a distributed environment only exacerbates the problem and lowers DS code quality. Full disclosure: my company Pachyderm [2] is trying to solve exactly the problem Jeff is talking about in the post. We've built a data processing platform on top of the container ecosystem. Basically, the data scientist has complete control over the runtime environment for their analysis since everything is bundled into a container. It scales to work for actual "big" data, but it also great for small teams that don't have massive infrastructure resources. [1] http://venturebeat.com/2015/04/01/the-geek-shall-inherit-the-earth-the-age-of-developer-defined-infrastructure/ http://venturebeat.com/2015/04/01/the-geek-shall-inherit-the... [2] github.com/pachyderm/pachyderm