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Big Data Goes Mainstream

100 million spreadsheet users can now tap into the power of the cloud

Virtualization Track at Cloud Expo

SQL was the first-generation Big Data tool, and MapReduce/Hadoop was the second-generation tool. Unfortunately, neither of these tools has the characteristics required to break into the mainstream of data analytics, where there are now over 100 million business professionals (non-programmers) grappling with exponentially growing data volumes that they simply can't handle.

However, a new third generation of tools for Big Data is now emerging that offer the scalability, parallelism, performance and data flexibility of tools like Hadoop, but, unlike Hadoop, can also continuously process realtime data streams. Moreover, these new tools are as easy to use as a spreadsheet.



Cloudscale

is the world's first realtime, massively parallel cloud platform for Big Data. The Cloudscale platform can be used by anyone who can use a spreadsheet - no databases, no programming - but is as powerful as anything used by the top parallel programmers at Google or the most experienced realtime analytics experts on Wall Street. Over 100 million Excel power users now have a seamless extension from their Excel desktop to the full power of cloud computing to handle their big data. The platform provides the elastic scalability and performance required to cope with anything from one-off personal data analysis tasks up to the most demanding large-scale analytics apps required by the world's leading organizations in business, web, finance, scientific research, and government.

With Cloudscale, an ordinary business user can now develop realtime analytics apps in minutes that would take a team of expert programmers months, and anyone can now sit in Excel and with one click launch an app on billions of rows of data that would normally take days to compute, but the user gets the answer from the cloud in 2 minutes.

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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