A few weeks back, Cisco in the UK released a report on the status of cloud adoption in the UK -- based upon market research by an independent market research consultancy. This report covered multiple customer segments, including retail, finance, public sector, service provider and others. I though many people across the world would be interested in some of the fascinating fact and figures, and conclusions, from this report. Market research is a key part of my role, and last year I blogged on some market research I did on cloud a while back (here and here). As a result I’m always interested in good pieces of market research, of which this “CloudWatch” report is an excellent example.
In this blog, I’ll point you to the full report and summarise and comment on some of the key conclusions.
If you’ve read some of my previous blogs, you’ll have seen that I work on our Cisco Cloud Enablement Services, our family of professional services offerings that have and are helping many customers across the world develop their cloud strategies, and realise their cloud computing architectures. These services are illustrated below, and include the recently announced Cloud Optimization Service.
Cisco Cloud Enablement Services - including the recently announced "Cloud Optimization Service"
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Tags: Cisco, cloud_computing, data center
Cisco recently announced the Nexus 7009 chassis expanding the Nexus 7000 family to 3 chassis. To refresh your memory on the Nexus 7000 family, here’s a quick at a glance comparison.
I often get asked, why Cisco introduced a 9 slot chassis when we already have a 10 slot chassis. The simple answer is – customers asked for a smaller form factor Nexus 7000 switch that delivers the high performance and resiliency that the Nexus 7000 family is known for.
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Tags: Cisco, data center, nexus, Nexus 7000, nexus 7009, Unified Fabric
In my previous blog, I had written about the use of mobile applications in the data center. Since then, I understand there is a new application for monitoring Cisco UCS on the Blackberry Playbook . Is this a trend? Is this a trend towards mobility or is it a trend towards consumerization of the work place? Yet another term I have heard is “Prosumerization” of the Enterprise. No matter which term is used by authors, the underlying shift is towards a simpler, user friendly approach to getting work done. If that means using smart phones, then so be it. Unlike my generation of workers, willing to put up with circuitous and acrobatic maneuvers to get things done, the younger generation is used to simpler interfaces and they are demanding the same from enterprise systems.
My one and a half year old niece who has grown accustomed to using iPhone Facetime to chat with my kids grimaced and acted surprised when she heard my daughter on the phone and did not see her face on a screen. I vividly recall having to wait patiently for hours to receive a subscriber trunk dialing call in order to talk to my parents when I was in college. The point is that we now have relatively sophisticated networks and tools, and user expectations are very high. Enterprise users expect their tools to just work, like cars and smart phones (have you read the iPhone manual?). There is also an expectation that the network is always on and can stream high resolution video.
Do you use your personal phone to access your work email? The line between work and home has blurred for most of us. Thanks to the network, we work from our home offices and with colleagues half way round the world at odd times of the day. No wonder we like to use the same tools and gadgets we are familiar with.
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Tags: consumer expectations, data center, Intelligent Network
As discussed in my previous post, application developers and data analysts are demanding fast access to ever larger data sets so they can not only reduce or even eliminate sampling errors in their queries (query the entire raw data set!), but they can also begin to ask new questions that were either not conceivable or not practical using traditional software and infrastructure. Hadoop emerged in this data arms race as a favored alternative to the RDBMS and SAN/NAS storage model. In this second half of the post, I’ll discuss how Hadoop was specifically designed to address these limitations.
Hadoop’s origins derive from two seminal Google white papers from 2003-4, the first describing the Google Filesystem (GFS) for persistent, massively scalable, reliable storage and the second the MapReduce framework for distributed data processing, both of which Google used to ingest and crunch the vast amounts of web data needed to provide timely and relevant search results. These papers laid the groundwork for Apache Hadoop’s implementation of MapReduce running on top of the Hadoop Filesystem (HDFS). Hadoop gained an early, dedicated following from companies like Yahoo!, Facebook, and Twitter, and has since found its way into enterprises of all types due to its unconventional approach to data and distributed computing. Hadoop tackles the problems discussed in Part 1 in the following ways:
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Tags: Big Data, Cisco, data center, Hadoop, NoSQL
If you have been a regular reader of just about any technology blog or publication over the last year you’d be hard-pressed to have not heard about big data and especially the excitement (some might argue hype) surrounding Hadoop. Big data is becoming big business, and the buzz around it is building commensurately. What began as a specialized solution to a unique problem faced by the largest of Web 2.0 search engines and social media outlets – namely the need to ingest, store and analyze vast amounts of semi- or unstructured data in a fast, efficient, cost-effective and reliable manner that challenges traditional relational database management and storage approaches – has expanded in scope across nearly every industry vertical and trickled out into a wide variety of IT shops, from small technology startups to large enterprises. Big business has taken note, and major industry players such as IBM, Oracle, EMC, and Cisco have all begun investing directly in this space. But why has Hadoop itself proved so popular, and how has it solved some of the limitations of traditional structured relational database management systems (RDBMS) and associated SAN/NAS storage designs?
In the Part 1 of this blog I’ll start by taking a closer look at some of those problems, and tomorrow in Part 2 I’ll show how Hadoop addresses them.
Businesses of all shapes and sizes are asking complex questions of their data to gain a competitive advantage: retail companies want to be able to track changes in brand sentiment from online sources like Facebook and Twitter and react to them rapidly; financial services firms want to scour large swaths of transaction data to detect fraud patterns; power companies ingest terabytes of data from millions of smart meters generating data every hour in hopes of uncovering new efficiencies in billing and delivery. As a result, developers and data analysts are demanding fast access to as large and “pure” a data set as possible, taxing the limits of traditional software and infrastructure and exposing the following technology challenges:
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Tags: Big Data, Hadoop, NoSQL