Actually, this is somewhat surprising. It's taking what many, MANY data scientists and analysts are actually using day-to-day and making it point-and-click to start in the cloud.

You can set these things up in AWS and GCE, but it requires a lot more work. Here is my top google hit for setting things up on AWS, from a relatively recent and maintained repository: https://gist.github.com/iamatypeofwalrus/5183133

Many data scientists just run their notebooks locally. Few have the luxury of Data Science Engineers on hand to help them through the complex world of provisioning and maintaining servers, even in this canned AWS world where spark clusters unfold themselves in EMR.

Azure is absolutely full of "here is a canned solution for _insert common engineer task with open source focus here_" buttons and it's really quite great as a result.

Also: Azure is doing a very good job of making it difficult to have a default-insecure instance. You don't make choices about encryption-at-rest, for example. That's forced on you. There is a lot more tooling around sharing accounts and giving users minimal ACLs rather than just doing the simple thing in AWS or GCE and giving out power user accounts.

It's simple stuff, of course, but this simple stuff is the simple stuff AWS has elected not to do, so...