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IT Briefcase Exclusive Interview: Keeping Your (Manufacturing) Head in the Clouds

IT Briefcase Exclusive Interview: Keeping Your (Manufacturing) Head in the Clouds

with Srivats Ramaswami, 42Q
In this interview, Srivats Ramaswami,...

IT Briefcase Exclusive Interview: New Solutions Keeping Enterprise Business Ahead of the Game

IT Briefcase Exclusive Interview: New Solutions Keeping Enterprise Business Ahead of the Game

with Sander Barens, Expereo
In this interview, Sander Barens...

IT Briefcase Exclusive Interview: The Tipping Point – When Things Changed for Cloud Computing

IT Briefcase Exclusive Interview: The Tipping Point – When Things Changed for Cloud Computing

with Shawn Moore, Solodev
In this interview, Shawn Moore,...

Driving Better Outcomes through Workforce Analytics Webcast

Driving Better Outcomes through Workforce Analytics Webcast

Find out what’s really going on in your business...

Legacy Modernization: Look to the Cloud and Open Systems

Legacy Modernization: Look to the Cloud and Open Systems

On the surface, mainframe architecture seems relatively simple: A...

“Cloud Coupling” [kuhp-ling] – Noun: Connecting One Cloud to Another

June 13, 2011 No Comments

I’d like to coin a new industry term: “cloud coupling”. This noun (and also a verb) describes the conjoining of two or more (generally large) data sets across various applications, cloud services and social media streams.

So, “cloud coupling“, say it with us now ☺

This term was thrown up after reading up on a company called SnapLogic this weekend. Describing itself as a ‘cloud connection’ company, SnapLogic recently released SnapReduce — a product designed to connect large data integration between business applications, cloud services, social media and the open source Hadoop software framework that supports data-intensive distributed applications.

See the connection yet?

Picture the scene –

1) A modern company has massive streams and repositories of both structured and unstructured data.

2) Our company not only use Hadoop, but also uses MapReduce, a technology developed by Google almost a decade ago to support distributed computing on large data sets held on computing clusters.

3) Mix the complexity of Hadoop’s computational model, the need to integrate with MapReduce and a combined lack of universal connectivity — and suddenly it becomes really hard for the company to reap business benefits from its so-called “big-data” operation.

Read More of Adrian Bridgewater’s Blog Post on ComputerWeekly.com

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