Visual Studio Code Remote Development allows you to use a container, remote machine, or the Windows Subsystem for Linux (WSL) as a full-featured development environment.

  • Develop on the same operating system you deploy to or use larger or more specialized hardware.
  • Sandbox your development environment to avoid impacting your local machine configuration.
  • Make it easy for new contributors to get started and keep everyone on a consistent environment.
  • Use tools or runtimes not available on your local OS or manage multiple versions of them.
  • Develop your Linux-deployed applications using the Windows Subsystem for Linux.
  • Access an existing development environment from multiple machines or locations.
  • Debug an application running somewhere else such as a customer site or in the cloud.

Brigit Murtaugh, a PM with VS Code, will walk you through the benefits of remote development workflows and then demonstrate how to set things up using VS Code, WSL, Windows Terminal, a remote desktop machine, and a Virtual Machine (VM).


  • 0:37 What is remote development? What are the benefits?
  • 1:40 Why is remote development a priority for VS Code?
  • 3:00  How do I set up a remote dev environment in VS Code?
  • 4:35 What is SSH? How do I do remote dev work with SSH?
  • 6:27 Demo: Use SSH to debug on a remote machine and virtual machine.
  • 17:30 What is WSL? Why is it good for remote dev work with VS Code?
  • 19:39 Demo: Use WSL to run a Python app in Linux but debug on Windows.
  • 26:33 What about remote development with Docker containers?
  • 27:30 Tabs vs Spaces?
  • 29:01 Where can I learn more?

.NET for Apache Spark empowers developers with .NET experience or code bases to participate in the world of big data analytics.

In this episode, Brigit Murtaugh joins Rich to show us how to start processing data with .NET for Apache Spark.

Time index:

  • [01:01] – What is Apache Spark?
  • [02:33] – What are customers using Apache Spark for?
  • [03:50] – What did we create .NET for Apache Spark?
  • [06:30] – Exploring GitHub data
  • [15:012] – Considering data processing in the real world
  • [18:26] – Analyzing continuous data streams

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