Rethink BI : Business Insights over Business Intelligence
The purpose of this business insights thought leadership blog is to share the HP point of view on industry trends such as Big Data and Real Time Analytics, and provide updates on key innovations and solutions.

Big Data architecture in the New Style of IT

Slaying the Big Data dragon is on everyone’s bucket list. In this New Style of IT, how does your data center keep up with the demands of Big Data? It’s an easy question to ask, but the answer is a bit more complicated than rip and replace. We recently asked Greg Battas, CTO of Business Intelligence Solutions for HP Converged Systems to demystify the Big Data conundrum. Along the way, we learned how cluster consolidation, processing power and software-defined storage are leading the way toward hardware built specifically for Big Data.

 

iStock_000023930618Small.pngIn this insightful series, you’ll gain an insider’s view of next-generation solutions to Big Data. We’ll start on common ground, and then move through a series of timely topics. First up, the most common challenge with Big Data today and how innovative technology can be deployed to handle it.

 

What’s a common challenge you see with Big Data?

GB: I see a lot of customers that have really adopted the idea of a converged infrastructure. But they will then put a Big Data cluster — usually a Hadoop cluster — alongside their converged infrastructure. This happens for a number of reasons. Typcial CI architectures are built around a switching network that is software defined, very often blades or a compute unit that can be allocated quickly for a particular workload, and then a shared storage environment. The idea here is we want to have any available compute node get to any data, so we can quickly provision up an application workload.

 

But Big Data is a little different.

 

Big Data assumes it will be on a network with a high cross-sectional bandwidth, meaning a lot of nodes shuffling large amounts of data between themselves. These nodes don’t necessarily require a lot of CPU power. In fact, many Big Data systems are set up with low-cost processors. And Big Data requires direct-attach storage and a hardware-resilient, simple server.

 

So, the architectures are different and they sit alongside each other. The problem for customers is they need to build multiple Big Data clusters to support different user groups and different Big Data needs. Even worse, the Big Data is often duplicated on these various clusters.

 

In our next post, we’ll explore possible solutions to this problem. But for now, check out this short video interview of Greg Battas at HP Discover 2013 in Las Vegas.

 

 

Greg_Battas_badge_176x304_tcm245_1428057_tcm245_1422290_32_tcm245-1428057.pngAbout Greg Battas

Greg’s background in solving business problems for customers — in particular those in the retail, telecommunications and financial services sectors — and in product development for relational database management systems, has played a critical role in helping bridge the gap between the viewpoints of IT and business decision-makers to explain how to use technology to solve challenging organizational issues.

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