Cloud computing has been revolutionising the IT industry by
adding flexibility to the way IT is consumed, enabling organisations
to pay only for the resources and services they use. In an effort to reduce IT capital and operational expenditures, organisations of all
sizes are using Clouds to provide the resources required to run their
applications. Clouds vary significantly in their specific technologies
and implementation, but often provide infrastructure, platform,
and software resources as services [25,13].
The most often claimed benefits of Clouds include offering resources in a pay-as-you-go fashion, improved availability and elasticity, and cost reduction. Clouds can prevent organisations from
spending money for maintaining peak-provisioned IT infrastructure that they are unlikely to use most of the time. Whilst at first
glance the value proposition of Clouds as a platform to carry out
analytics is strong, there are many challenges that need to be overcome to make Clouds an ideal platform for scalable analytics.
In this article we survey approaches, environments, and technologies on areas that are key to Big Data analytics capabilities and
discuss how they help building analytics solutions for Clouds. We
focus on the most important technical issues on enabling Cloud
analytics, but also highlight some of the non-technical challenges
faced by organisations that want to provide analytics as a service
in the Cloud. In addition, we describe a set of gaps and recommendations for the research community on future directions on Cloudsupported Big Data computing.