Meeting the Needs of Growing Analytical Demands Requires a New Software and Hardware Approach.
Today’s mobile and cloud era involves an ever-growing number of devices and connections to the Internet. This creates a new challenge for IT environments, which need to handle and process the masses of data being produced.
The challenge is to leverage the large amount of structured, semi-structured and unstructured data that is being generated, especially in connection with e-commerce, social media and the Internet of Things (IoT). The idea is that more data should lead to more accurate analyses, leading to better decisions and greater operational efficiencies.
However, most IT organizations are finding themselves faced with data sets that are too immense and complex to be processed using most relational database management systems and desktop statistics and visualization packages. As the amount of data continues to grow exponentially, organizations increasingly rely on solutions – such as Hadoop and Cassandra, which are built to handle immense data volumes – to present meaningful and actionable results.
These emerging software analytics platforms often share one commonality: They rely on distributed and scale-out architectures.
Unlike traditional data analytics solutions, these new frameworks perform parallel queries that run concurrently across tens, hundreds, or even thousands of servers. Successful implementations often hinge on mapping out the right strategy for deploying and managing the infrastructure necessary to support this new breed of analytics.
A fully virtualized infrastructure can provide the agility needed to provision additional compute instances dynamically while also simultaneously allowing non-analytics workloads to run side by side. This negates the requirement to purchase and manage application-specific hardware. In addition, policy-based configuration practices provide the delivery of workloads in a matter of minutes, providing a new level of control over resource placement.
With its innovative rack-scale architecture, Stratoscale provides the capabilities needed to confidently move ahead with any big data initiatives. By optimizing the deployment and management of virtualized Hadoop installations, Stratoscale allows organizations to get back to focusing on using big data insights to improve decision-making and increase productivity.
Stratoscale helps you get the most out of your investment in Big Data:
Rack-scale hyper-convergence leverages commodity compute, storage and networking components to create a single set of resources delivering the right resources to workloads, just in time
Businesses can reduce infrastructure management complexity by consolidating Hadoop deployments
Concurrent Hadoop installations can be isolated and secured, for multi-tenant use cases