Despite common misconceptions Microsoft now has extensive interoperability with open source technologies for example you can run a php application on Azure, get support from us to run RedHat, SUSE or CentOs on Hyper-V and manage all your applications from System Center 2012.
So how are academics using these resources and services
Prof. Baesen's from Katholieke Universiteit Leuven (a.k.a. KU Leuven) in Belgium published yesterday a paper called "Beyond the hype: cloud computing in analytics". looking at Machine Learning. KU Leuven has set up a benchmarking experiment using Machine Learning techniques used in analytics, the Microsoft Windows Azure cloud platform and the middleware of Techila Technologies. The results were compared with those obtained in a non-parallelized setup. The results show that significant analysis speed-ups can be gained when performing computational tasks in cloud.
Researchers have amazing opportunity now with Microsoft and Openness additionally were extending this approach to the world of big data with Hadoop.
As you know from my previous posts Hadoop uses map reduce, the key to the power and scability of Hadoop is that it applies these map reduce concept on large clusters of servers by getting each node to run the functions locally, thus taking the code to the data to minimise IO and network traffic using its own file system – HDFS.
As your all aware there are lots of toolsets for Hadoop, many of these are built on Hive which presents HDFS as a data warehouse that you can run SQL against and the PIG (latin) language where you load data and work with your functions.
Microsoft are developing in conjunction with developer Horton Works the following functionality:
At the time of writing there these tools are still in development and there is only “by invitation” admission to Hadoop on Azure. If your interested in this please do get in touch. simply email email@example.com