Big Data deployments shouldn’t be intimidating. You shouldn’t have to have a Big Data PhD to analyze and make sense of your most critical data.
But let’s face it – a Big Data deployment can be complex and costly. At Rackspace, we’ve partnered with several Big Data players to offer you options on how to capture and analyze your data with fewer headaches. The Rackspace Hybrid Cloud gives Big Data companies the best of all worlds – high performance and customization across a hybrid cloud architecture that offers public and private clouds and dedicated bare metal.
And, today, we welcome a new Big Data partner to the family. We’ve teamed up with SiSense to power its Prism 10X business analytics software, which offers In-Chip Analytics in the Rackspace Hybrid Cloud to dramatically reduce the cost and complexity of Big Data cloud deployments and remove the obstacles typically encountered with Big Data in the cloud.
What’s so cool about this? Prism 10X can run terabytes of data on a single Rackspace node, turning commodity machines into “data monsters.” That means you get terabyte-range analytics workloads in the cloud without the headaches of setting up complex clusters and acquiring or maintaining costly, clunky hardware. Prism 10X has been proven to reduce Big Data Analytics costs to as little as $10 per TB per day.
Big Data Analytics is critical to Rackspace and our customers. With the Rackspace Hybrid Cloud as the platform, SiSense has the ability to tailor the platform to its specific workload requirements, has dedicated resource allocation with no multi-tenanting and receives enterprise grade support and SLAs. The result is swift deployment and seamless scaling of the underlying architecture without the massive costs of a typical Big Data deployment. With SiSense running on the Rackspace Hybrid Cloud, users can analyze 100 times more data at 10 times the speed of traditional in-memory solutions.
We look forward to working closely with SiSense and the rest of our Big Data partners to continue to help you overcome the hurdles of Big Data Analytics.