A Better Public Cloud Service for Modern Workloads
Software Composable Infrastructure delivers a cost effective, high performance platform for Big Data in the cloud. How can a public cloud provider offer a big data infrastructure that delivers bare metal, dedicated instance performance while also allowing customers to easily scale up or down as needed and capture the economic advantage of a shared resource?
In this white paper you will learn how Software Composable Infrastructure delivers the performance of dedicated bare metal clusters, but with the elasticity and cost effectiveness public cloud providers have come to rely upon.
Software Composable Infrastructure for Clusters
The developers of the Hadoop/Big Data Architecture at Google and then at Yahoo were looking to design a platform that could store and process a vast quantity of data at low cost. To achieve this, they developed several key principles around system architecture that Enterprises need to follow to achieve the goals of Big Data applications such as Hadoop, Spark, Cassandra, etc.:
Falling Out of the Clouds: When Your Big Data Needs a New Home
Running your Big Data Analytics in the Cloud? Is it becoming expensive? In this informative white paper for Data Center professionals, get exclusive insights into some of the tradeoffs of running Big Data workloads in the cloud and guidelines for when to bring it in-house. While AWS and other cloud providers can appear to offer flexibility, production Hadoop frameworks can suffer in the cloud due to lack of control, predictability and high costs.
This white paper will help you better understand when and why you may choose to move your Big Data out of the cloud, and offers insights on how to avoid the long deployment times and costs of managing your own clusters. You’ll discover how to protect your hardware investment, improve predictability, and gain increased scalability by leveraging a unique approach that separates compute and storage resources.