hadoop

In this blog-post Bradford Stephens, Drawn To Scale's founder, answers a series of technical, business and personal questions to give an overview of what Drawn To Scale is and where it is going. Who are the founders? What is their background, technology and business model? How were they going to manage other people's big data? Can one tool fit the demands from a broad range of data challenges that different businesses are seeing?

In a few of my recent posts I have covered the ease of deploying clusters of Hadoop and Cassandra using Whirr. With Whirr you can simply write a

In this post I will define what I believe to be the most important projects within the Apache Projects for building scalable web sites and generally managing large volumes of data.

Here I demonstrate, with repeatable steps, how to fire-up a Hadoop cluster on Amazon EC2, load data onto the HDFS (Hadoop Distributed File-System), write map-reduce scripts in Ruby and use them to run a map-reduce job on your Hadoop cluster. You will not need to ssh into the cluster, as all tasks are run from your local machine. Below I am using my MacBook Pro as my local machine, but the steps I have provided should be reproducible on other platforms running bash and Java.

There are data sources out there, but which data source you choose depends on which technology you wish to get experience working with. The experience should be of the technologies you are using, rather than what the data is. Certain datasets pair better with certain technologies. Simulating the data can be another approach. You just need a clever way of generating and randomizing your fake data. Thirdly, you can use a hybrid approach. Take real data and replay it on a loop, randomizing it as it goes through. Simulating the Twitter fire-hose should not be too hard, should it?