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NoSQL + Hadoop Without the Headaches: MapR Technologies Announces New Big Data Platform for HBase #stratany2012

Live from O’Reilly Strata Conference and Hadoop World: MapR M7 brings enterprise-grade reliability and performance to HBase, adding integrated snapshots, mirroring, instant recovery, and delivering consistent, low latency.

The Big Data Treasure Chest

Within most enterprises there sits a large and precious treasure chest. Inside that chest sits data which holds great, untapped value. While CEOs might have recognized this a few years back, they didn't talk about it much because it was impractical and near impossible to extract wealth from the data.

Not so much anymore. Hadoop’s speedy data processing and analytics capabilities have changed that dramatically.

Enterprises worldwide are now using, or plan to use, Hadoop to unlock the value in their big data stores. But doing so isn't simple and it’s not without risks. This has opened the door for vendors to add reliability, stability and host of bells and whistles to Hadoop. MapR is one of the companies that leads that space.

MapR Announces New Big Data Platform

This morning at the O’Reilly Strata Conference + Hadoop World 2012 MapR unveiled M7 which promises to bring unprecedented Hadoop and NoSQL capabilities together on an easy, dependable and fast platform. With MapR M7, Big Data operations ranging from batch analytics to real-time database functions can be performed with enterprise-grade reliability and protection.

“We've made Hbase enterprise grade,” says Tomar Shiran, director of product management at MapR, explaining that MapR M7 can withstand multiple software and hardware outages and keep applications running without needing any administrator intervention at all. It’s the only Hadoop distribution that provides instant disaster recovery and full data protection with snapshots and mirroring, he adds.

But MapR M7 doesn't stop there, it also provides constant and consistent performance at unprecedented levels because it doesn't require compactions and because its innovative data structures minimize the read- and write-amplification factor. Updates and inserts are also much faster.

“We start with Hadoop and by adding our own innovations we make it much better,” says Shiran. 

MapR & the Google Compute Engine, Plus More

And while we know that every vendor has a pitch to explain why they are the best, MapR points to its recent wins. They snagged an exclusive to run Hadoop services on the Google Compute Engine last June, which says a lot because Google wrote the paper on Map Reduce. In that same month, Amazon announced that MapR’s Hadoop distributions would be available on Amazon Web Services vs. its own Elastic MapReduce service that runs on Apache Hadoop.

MapR has other impressive enterprise level Big Data ground-breakers on its client list as well, such as the Rubicon Project, a digital advertising infrastructure company that automates buying and selling for the global online advertising industry; Ancestry.com which crunches human genome, text and other data to lead you to your ancestors, and many, many others.

MapR is setting a new benchmark in the world of Enterprise-grade Hadoop. It will be interesting to see if anyone is able to exceed it before it “bests” itself.

 
 
 
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