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The Client-plus-Cloud Revolution

Powerful local computing together with massively scalable parallel cloud computing is a real win

Data is growing exponentially everywhere - in business, web, finance, government, science, and in the world of sensors and smart grids.

Speaking earlier this week at OSBC, Tim O'Reilly said  "The future will be all about who has most data, and who is able to extract meaning from it and deliver it in real time".  He noted that the IT industry is now in the process of being reinvented around the idea of realtime analysis of "Big Data" in the cloud, as a must-have adjunct to the much more limited kinds of data processing and analytics that can be performed on desktop PCs or mobile devices.

Today, every organization needs to become as expert at exploiting "Big Data" as leading edge companies such as Google and Facebook have become. So how are they going to get in the game? How is the ordinary business professional, for example, going to handle the explosively growing volumes of data that they now have to deal with.

The world’s #1 tool for analyzing data is Excel. Over 100 million Excel users analyze data constantly with it, and they love it – no databases, no programming, no IT department to deal with. But for Big Data, Excel is nowhere near enough. And a lot of Big Data is now realtime streams, so databases, data warehouses, and programming tools like MapReduce/Hadoop are of no use either.

We clearly need a new generation of tools and platforms for Big Data, but they need to be as easy to use as Excel, given that almost all of those now grappling with Big Data are non-programmers.

  • Wouldn’t it be amazing if you could sit in Excel and develop Big Data apps in minutes that would take even a team of expert programmers at Google or on Wall Street months to develop!
  • Imagine you could sit in Excel and with one click launch an app on billions of rows of data that would normally take days to compute, but you get the answer back in less than a minute!
  • Wouldn't it be fantastic if you could sit in Excel and (1) run an app continuously on the Amazon cloud analyzing live stockmarket data looking for significant events or patterns, and at the same time (2) be running an app continuously on the Azure cloud analyzing the latest newswires, blogs and tweets in realtime to detect significant shifts in sentiment, and (3) be streaming live data from both apps into your spreadsheet in parallel, and correlating them in realtime to get a huge competitive advantage.

At Cloudscale we set out to change the world of Big Data by cracking this problem. It quickly became clear that a cloud-only platform (no local client) would really only deliver part of what was potentially achievable. As Dan Reed of Microsoft noted recently, a "Client-Plus-Cloud" platform offers so much more - powerful local computing and massively scalable parallel cloud computing. When the local client is as powerful as Excel, rather than just a simple browser app, this is a real win.

On Monday at DEMO, Cloudscale is launching Cloudcel – the world’s first realtime, massively parallel cloud platform for handling Big Data. It can be used by anyone who can use Excel, but is as scalable and powerful as anything on the planet. It's also the first product that demonstrates the full power of integrated Client-Plus-Cloud computing. With Cloudcel, you can use desktop Excel in the normal way, but can also now seamlessly tap into the scalability and massive parallelism of the cloud, entirely from within Excel, to handle your Big Data.

More Stories By Bill McColl

Bill McColl left Oxford University to found Cloudscale. At Oxford he was Professor of Computer Science, Head of the Parallel Computing Research Center, and Chairman of the Computer Science Faculty. Along with Les Valiant of Harvard, he developed the BSP approach to parallel programming. He has led research, product, and business teams, in a number of areas: massively parallel algorithms and architectures, parallel programming languages and tools, datacenter virtualization, realtime stream processing, big data analytics, and cloud computing. He lives in Palo Alto, CA.

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