Two examples: 1) I wanted to provide a Jupyter notebook with IBM DB2 support for a university course. (Why DB2? Because its optimizer can transparently use mat

Two examples: 1) I wanted to provide a Jupyter notebook with IBM DB2 support for a university course. (Why DB2? Because its optimizer can transparently use materialized views for query optimization which PostgreSQL can't AFAIK.) IBM provides a Jupyter magic which requires the Python package ibm_db. ibm_db requires a DYLD_LIBRARY_PATH hack which macOS doesn't support unless you disable System Integrity Protection. I don't want to disable SIP on my system and I can't ask our students to do that. A Docker image provides a convenient solution to the problem. 2) I do a lot of disparate project development using Flink and Hadoop. These get deployed on Linux machines. My preferred way to develop on my Macbook is to boot up a headless Ubuntu system inside VirtualBox, SSH into it, and then start a TMUX session. This has a number of advantages. a) The dev environment more closely resembles the production environment. b) I can setup my TMUX session, save the state of the VM, and then restore it months later to the exact state just by booting up the VM. c) I don't have to pollute my macOS environment with dev tools that I rarely use. It's not strictly necessary but it's actually quite convenient. I can even do development in IntelliJ on macOS, run the services inside the VM, and thanks to remote JVM debugging use the IntelliJ debugger on macOS to step through the code running inside the JVM.