Next week is Cloud Connect in Santa Clara and Cisco’s Cloud Software group will have a big presence.
While we have plenty to talk about on how Cisco is helping customers build their cloud, we also want to listen to our customers plans and needs. We are bringing some of our engineers and architects so you can engage directly with them. There are three things you can see next week.
CITEIS -- Cisco’s, in production, private cloud.
See how it was built, the results in agility and cost, and best of all see a demo. Not a fake demo but the real thing.
Of course, we will also be showcasing our award winning cloud automation software, Cisco Intelligent Automation for Cloud (CIAC) (formerly newScale and Tidal), which provides the self-service catalog and orchestration to our private cloud
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Tags: Big Data, CIAC, cloud portal, Hadoop, intelligent automation, newScale, OpenStack, Tidal, Tidal Enterprise Scheduler, unified management, workload automation
Big Data’s move into the enterprise has generated a lot of buzz on why big data, what are the components and how to integrate? The “why” was covered in a two part blog (Part 1 | Part 2) by Sean McKeown last week. To help answer the remaining questions, I presented Hadoop Network and Architecture Considerations last week at the sold out Hadoop World event in New York. The goal was to examine what considerations need to be taken to integrate Hadoop into Enterprise architectures by demystifying what happens on the network and identifying key network characteristics that affect Hadoop clusters.
The presentation includes results from an in depth testing effort to examine what Hadoop means to the network. We went through many rounds of testing that spanned several months (special thanks to Cloudera on their guidance). Read More »
Tags: Big Data, Cisco, Cloudera, data center, Hadoop
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
Today’s IT organizations face a broad set of challenges today.
- How to deal with the proliferation of end-user devices? (smartphones, tablets, etc.)
- How to deal with the proliferation of virtualization and it’s new operational model?
- How to adapt to requirement for new application traffic patterns (east-west, VM mobility)?
- How to manage the edges of their networks as work/life locations blur?
- When do they decide to deliver a business need via internal resources vs. external resources?
- With all this technology change happening so rapidly, how do they align their teams?
Tags: Big Data, Cisco UCS, Cloud Computing, Cloud-Ready Network, Consolidation, FabricPath, Hadoop, LISP, nexus, OTV, virtualization