Big Data Storage Solutions – New Whitepaper!

The abundance of Big Data holds the potential for high value analytical insight never before available.  Mining Big Data is an immediate, critical challenge for many businesses and research organizations.  Analysis of those data creates new data sets and metadata that must be stored to be acted upon.  A new generation of high performance approaches to modeling, optimizing, text mining and statistical analysis are required to turn these terabytes, petabytes and even exabytes of information to actionable analyses.

Big Data Analytics’ Need for Speed and Security

Nearly all areas of research need analytics that are faster, more powerful and cheaper — to address massive data sets growing geometrically.  Data Scientists who utilize public or general-purpose cloud resources for capacity and scalability quickly realize how data transfer rates pose a major limitation.  Many have moved back to private HPC, whether cloud or purpose-built infrastructure, to surpass these limits.  Data must be moved from primary sources to multiple researchers quickly for any time sensitive analysis.  New approaches in computing architecture, both hardware and software, are constantly being developed to address these mushrooming data demands while providing the scalability and performance required.

Simultaneously, high value data must be protected.  Whether the requirement is high availability In the face of hardware or infrastructure failure, or reliable and immediate retrieval of archived data, systems must be designed to accommodate these requirements.  Data scientists must also protect data from intrusion, theft, or malicious corruption.  Due to the sensitivity of the subject matter involved in many areas of research, privacy, security and regulatory compliance are factors that drive decisions away public and shared cloud environments and towards private cloud and protected infrastructure.

GPFS an Excellent Solution for Big Data Infrastructure Needs

Huge improvements in performance have been achieved across these networked processors and storage disks using parallel application and file systems (GPFS) offering almost unlimited scalability, but most in-house IT personnel don’t have the skills or experience needed to architect, build and operate an infrastructure to suit Big Data needs.