Why is data preparation so hard and are we getting worse at it?

Some statistics for the aperitif:

Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets. New York Times, 2014. No source for the statistics.

Analysts will still spend up to 80% of their time just trying to create the data set to draw insights. Forrester, 2015. No source for the statistics.

Since the popular emergence of data science as a field, its practitioners have asserted that 80% of the work involved is acquiring and preparing data. Harvard Business Review, reprinting the statistic from Forbes in 2016. Forbes cites a “survey of about 80 data scientists was conducted for the second year in a row by CrowdFlower.”

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Your first DIY Hadoop cluster

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.

Hadoop Salaries in the UK (Source: IT Jobs Watch)

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While these days there seem to be a separation between analyst and administrator/developer roles on the market, I am of opinion that either role has to be aware of the objectives of the other. That is: an analyst should understand the workings of a Hadoop cluster, just as a developer needs to understand the demand an analysis will put on the worker nodes. It’s very similar to a skilled Business Intelligence specialist that appreciates the impact a database design has on the speed of query processing and the availability of the system. That philosophy is the why behind this post: getting to know Hadoop by configuring a cluster yourself. You could be creating a cluster simply because you want to see how it’s done, or perhaps you are looking to extend the processing power of your system by an extra server. Continue reading “Your first DIY Hadoop cluster”

My computer AKA my first big data machine

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.

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