Have you used Google Docs? How is it different from documents edited in a desktop editor? Have you filed taxes online? How is the experience different from doing your taxes in a desktop program? There are lots of benefits, but one key aspect is collaboration. You don't have to email documents back and forth, you can work on your taxes alongside your accountant. When I first started doing scientific research, collaborating on a single result was painful. I'd write some code and create a graph, and then I create a snapshot, create the image file, put it into a dock, create a PDF, and send it to my collaborator. A few hours later, my colleague would say, "Looks great, but could you add one more year of data? It looks sparse." I'd go through the process all over again. Why? Because a PDF I had sent along was not editable. Round trips took a long time. Enter Python notebooks. I'd write the code, create the graph, write some commentary, and send the notebook link to my colleague. When my colleague wanted to add one more year of data, she would simply edit the cell, look at the new graph, and then say, "See, looks a lot better," and that was great, we now had a better notebook to do the next step. But one problem with traditional notebooks, who runs the server that hosts these pages? Who's machine? If it's mine, and the machine goes to sleep, then my colleague can't work. When your Datalab notebook is hosted in the Cloud, you can develop together quite easily. Just as Google Docs are available even when your computer isn't on, so do our Datalab notebooks when you run them on the Cloud. To share a notebook within a project, other users can simply Datalab connect to the VM and work using the URL. Another way to share notebooks is through revision control systems such as Git. Cloud also frees you from being constrained by hardware limitations. Not only can you run Datalab in any computer engine instance you want, you can also change the machine specs after it's been provisioned. You can go to the web console and find the running VM, stop it, and restart it with a new machine configuration. As a developer, to work in Datalab, you simply connect to the VM that's running the notebook server. The notebooks themselves can be persisted in Git, so you can delete the VM if you don't need it anymore. When you run the notebook, that computation is carried out on the VM.