It is common to blame vendors and IT service providers with hidden costs. It is true that maintenance fees, transformation services, new training or adjacent system upgrades are required when new equipment is installed.
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I am a proud father of 5 kids. My 4 oldest are college graduates with meaningful careers in recreation, magazine editing, teaching/coaching and law practice. My youngest is a freshman in high school, and I am always looking to strike-up a discussion around IT (since none of my other kids chose the field) and options for a future career. I find that high school course selection can have an impact with college and eventual career choices.
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I have had several blog entries on cloud services and the risk of just shifting costs to the cloud. There are some other entries on identifying your current costs of a class of storage (say tier 3) to accurately compare and contrast the exact same costs from a cloud vendor.
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I have been developing a small mini-series on the economics of big data, with a focus on the storage approach used in Hadoop and Azure architectures. The intro blog, case study #1, and a review of bare-metal analysis have been posted to this blog over the past few weeks. I will wrap up this series with another case study, this time with a Hadoop environment.
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I’m out of the office this week—and I plan on continuing my big data case study series as soon as I return—but quickly wanted to reiterate something Claus posted last week.
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I have put together a couple blog entries reviewing some cost analysis that I did 2-3 years ago around Hadoop and Azure storage/server architectures–specifically how we worked with customers to reduce the costs of these environments (in part) with enterprise-class storage. It goes without saying—but I will anyway—the focus of these economic models and case studies was on the deployment and costs of the storage infrastructure. Some of these new cloud/big data environments do not use RAID overhead or distribute data across hundreds of nodes and disk clusters to perform the work. As I did this work, we took a myopic view of just the storage hardware aspect of these environments. I guess you would expect that from an HDS employee.
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Last week I posted an introductory blog about big data and some work I had done a few years back in this space (before it was called big data). I have a couple of these large TCO assessments in my library, but will just share 2 or 3 of these that have the easiest story to tell, and make the point around price and cost of big data storage infrastructures.
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There is plenty of talk in the press today about big data, analytics and our next new wave for IT. I would like to present 2-3 blogs on a small but important subset of the big data world: storage infrastructure (and more importantly, optimal storage architectures). I will use our storage economics approach for the definitions of “optimal”, meaning you can address optimized storage from other dimensions as well (resiliency, scale, performance, etc.) as you develop big data strategies.
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I had a conversation with a colleague yesterday on budget consumption, and then saw this shark-eating-shark photo in National Geographic:
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There has been great news about nondisruptive migration capabilities, and Hu has a great post that you can read here on the options now available from HDS. Hu quoted me on a rate of $15K per TB for traditional migration, and I would like to address this rate and the research we have done on costs associated with various migration options.
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