When it comes to Big Data, one of the more significant challenges is deciding where to put all that data. After all, once it reaches a certain size, it becomes both impractical and expensive to move it. Given the cost of housing all that data, a lot of organizations are naturally turning to the cloud to store it. Naturally, wherever the data is, the analytics applications are sure to follow.
This week, Revolution Analytics announced that it is making its commercial implementation of the open source R programming language for building analytics applications available on the Amazon Web Services public cloud.https://o1.qnsr.com/log/p.gif?;n=203;c=204663295;s=11915;x=7936;f=201904081034270;u=j;z=TIMESTAMP;a=20410779;e=iDavid Smith, R evangelist at Revolution Analytics, says regardless of the format it’s in, Revolution R provides a framework for building Big Data applications that are often more easily shared on a public cloud. While developers could certainly use the open source R programming language on their own to build applications, Revolution Analytics provides enterprise IT organizations with a “write once deploy anywhere” functionality that enables data analysts and IT teams to write code once and deploy it anywhere in a variety of data management platforms. In fact, Revolution Analytics claims to be the only Big Data Big Analytics platform to include a library of algorithms that run inside the Cloudera and Hortonworks Hadoop platforms and in Teradata databases.
Users of the AWS service can run computations on data sets up to 1TB on Windows and Linux servers that access data stored in the Amazon Simple Storage Service (S3) and Amazon Relational Data Service (RDS) service. Utility pricing for both versions starts at $1.25 per core per hour.
As part of a larger trend that is witnessing the deployment of all kinds of enterprise-class software on AWS, it’s clear that a lot of traditional enterprise application workloads are moving to the public cloud. At the forefront of the movement, of course, are Big Data applications that most IT organizations simply don’t have the IT infrastructure resources on hand to support. The challenge facing IT organizations once that occurs is figuring out how to manage a massive amount of data that now runs outside the four walls of the traditional enterprise data center.