Comments on: Does big data really need custom hardware?

By: Stefan bernbo

Stefan bernbo — Mon, 29 Oct 2012 18:10:06 +0000

Interesting article and a good question, do Big Data workloads really need custom hardware? We don’t think so. That is why Compuverde has developed a hardware-independent Big Data solution. With Compuverde’s software solution, load is distributed evenly to all storage nodes instead of just the one gateway, thus eliminating the bottleneck problem and improving access speed at the same time. Suddenly, companies are able to confidently use a cheaper hardware that uses up to 50% less energy. I would be interested to hear what you think about our technology: http://www.youtube.com/watch?v=9916BeLq4MM

By: Software Development Services

Software Development Services — Fri, 12 Oct 2012 06:36:05 +0000

Customization of hardware resource is definitely a win win condition for big data. Conventional architecture of systems or hardware resources could not support the processing of petabytes of data as under load conditions these systems slows down, then how can we expect that under conditions of big data, which is far way large in amount, these hardware specs will be able to support and provide smooth processing.

By: iTrend

iTrend — Wed, 10 Oct 2012 17:59:37 +0000

Specialized hardware makes sense in some cases, we event designed some:

http://www.itrend.tv/single-project-02.html

But the big cabinet-size boxes being offered by larger manufactures are very inflexible, from specs to pricing. Problem with big data is – no tasks are the same. Natural language processing, log crunching, fraud detection – all require different combinations of processing power, I/O capabilities, storage capacity. A monolithic “appliance” would work well for some tasks but not others.

What can succeed is modular architecture, where a cluster is assembled from lego blocks, based on specific job requirements.

By: thierryhubert

thierryhubert — Tue, 09 Oct 2012 21:05:55 +0000

I think that Big Data is also a challenge for consuming information on highly time-sensitive social networks like Twitter. I actually discover of this article via tweather.co the TechCrunch SAP Big Data Startup of the Year. This is the report that used and created for Big Data http://tweather.co/bigdata The issue of storage and scalable infrastructure for Big Data analysis in this space remains uncertain at this juncture. I am struggling to manage costs on AWS and I am looking for my tipping-point to invest in infrastructure or remain in the cloud.