Summary: Intro | Linux VM Setup | VM Networking | Extending a Hadoop Cluster
At times I wish I had started my journey with Big Data earlier so that I could enter the market in 2008-2009. Though Hadoopmania is still going strong in IT, these years were a gold era for Hadoop professionals. With any sort of Hadoop experience you could be considered for a £80,000 position. There was such shortage of Hadoop skills in the job market that even a complete beginner could land a wonderfully overpaid job. Today you can’t just wing it at the interview; the market has matured and there are many talented and qualified people pursuing careers in Big Data. That said, after years, the demand for Hadoop knowledge is still on the rise, making it a profitable career choice for the foreseeable future.
Summary: Intro | Virtualisation Software | Cloudera’s QuickStart VM | Importing a VM
In this post, I will introduce Virtual Machines: the core platform of every data scientist. If you, like me, get to experiment with different technologies at work, you are familiar with Virtual Machines. VMs are the best way of getting to test something out without having to install it on your computer and risking messing up your working environment. In its essence, a VM is like a mini (virtual!) computer you put on your computer; that computer has its own environment, like Windows, Linux or MacOS, and it would usually come with a bunch of pre-installed and configured tools, so that you don’t have too worry about any (or much) setup. So you might have a Windows machine installed on your actual Windows machine, and while these two share computing resources and space, they are separate instances of Windows. Plus, the virtual machine you can delete or change as you please, you can have many and, by definition, this has no impact on your original working environment.