Archive for May, 2012
Last month HDS introduced the Hitachi Compute Blade 500 (CB500) with the latest Intel technology. An interesting new technology that was also introduced with this product was an embedded Ethernet switch. I have asked Gary Pilafas, who has responsibility for Hitachi converged solutions and is a frequent contributor to this blog, http://blogs.hds.com/hu/2011/06/converged-data-solutions-from-hitachi-gary-pilafas.html to tell us [...]
There are many examples of how big data brings value to companies large and small. Collecting data from new sources and correlating the data to generate information that helps grow the business and provide a competitive edge. I will be providing several examples of this during my series on big data. However, the value of [...]
As long as I have been in storage, there have been predictions that storage will be commoditized and all you will need are JBODs.(JBOD stands for Just a Bunch Of Disks. I first heard this term when I was working for IBM over 30 years ago. I was working for the IBM storage division at [...]
Randy Kerns, a blogger on Storage Soup and also a Senior Strategist at Evaluator group, recently blogged about Confusion Over Storage Consolidation.
Last month Hitachi announced enhancements to HCP, which is a major new release of our Hitachi Content Platform. There were some major enhancements which were covered by Michael Hay in HCP and HDI, A Monster Release and by Ken Wood in HCP Announcement with an Archiving Angle on the Techno Musings blog.
In past posts we talked about Big Data Volume and Velocity requirements and how they could be addressed with Hitachi Data Systems block, file and content storage. Today we will be looking at big data variety.
This morning I accompanied a young person to the Superior Court in San Jose. This Court is located at 99 Notre Dame in San Jose. Once we went through the metal detectors we joined a long line of people in a crowded waiting room. What surprised me was the display along the south wall, which [...]
Velocity is the next attribute of big data according to Gartner and IDC which I cited in my post Big Data Origins.
Referring back to my last post, I am continuing my series on big data where we are looking at the dimensions of big data: volume, velocity, variety and value, and what we need to do to address them. The first dimension has to do with the “big” in big data – volume.