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
What provisioning the Cloud infrastructure and cooking have in common…
I like to cook. Sometimes, I’ll grab whatever ingredients I have on hand, put them in a Dutch oven, throw in a few spices, and make a delicious casserole that can never be repeated. At other times, I’ll follow a recipe to the letter, measure and weigh everything that goes in, and produce a great meal that I can repeat consistently each time.
When provisioning servers and blades for a Cloud infrastructure, the same 2 choices exist: follow your instinct and build a working (but not repeatable) system, or follow a recipe that will ensure that systems are built in an exacting fashion, every time. Without a doubt, the latter method is the only way to proceed.
Enter the Cisco Tidal Server Provisioner (an OEM from www.linmin.com) , an integral component of Cisco Intelligent Automation for Cloud and Cisco Intelligent Automation for Compute. TSP lets you easily create “recipes” that can be easily deployed onto physical systems and virtual machines with repeatability and quality, every time. These recipes can range from simple, e.g., install a hypervisor or an operating system, to very complex: install an operating system, then install applications, run startup scripts, configure the system, access remote data, register services, etc.
Once you have a recipe (we call it a Provisioning Template), you can apply it to any number of physical systems or virtual machines without having to change the recipe. Some data centers use virtualization for sand box development and prototyping, and use physical servers and blades for production. Some data centers do the opposite: prototype on physical systems, then run the production environment in a virtualized environment. And of course, some shops are “all physical” or “all virtual”. Being able to deploy a recipe-based payload consistently on both physical and virtual systems provides the ultimate flexibility. Yes, once you’ve created a virtual machine, you’ll likely use VMware vSphere services to deploy, clone and move VMs, but as long as you’re using TSP to create that “first VM”, you have the assurance that you have a known-good, repeatable way of generating the golden image. When time comes to update the golden image, don’t touch the VM: instead, change the recipe, provision a new VM, and proceed from there.
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Tags: Cloud Computing, data center provisioning, disk imaging, intelligent automation, job scheduling, linmin, orchestration, self-service, server provisioning