UPDATED 15:00 EDT / JUNE 19 2012

MapR Teams with Amazon for Hadoop-as-a-Service

There’s a lot of talk about organizations catching up with their data using technologies such as Hadoop, but for the most part that only applies to larger companies with the resources to maintain such an environment. There’s a big opening for more affordable analytical solutions, and MapR just jumped on the bandwagon.

There are quite a few ‘light’ solutions out there that offer hosted big data services that have a lot of capabilities, but still can’t match the real deal. Via a newly announced partnership with Amazon Web Services however MapR is offering the best of both worlds.

The company has made its Hadoop distribution available on AWS.  Customers can run the software in an Amazon Elastic MapReduce (EMR) cluster for the standard fee if they’re using the free M3 edition, or for an added hourly price if they want the commercial M5 version.  The company also states that a number of other services are included in this bundle: S3, Amazon DynamoDB and Amazon CloudWatch have all been optimized for MapR’s solution.

The value prop is a pretty unique one. The company is selling a model that can be extremely cost-effective for SMBs as well as for organizations that want to supplement their existing deployments; on top of that, there’s also an impressive list of technical advantages. MapR leverages a no-NameNode architecture to avoid the single point of failure and provides snapshot recovery and mirroring, while AWS handles the provisioning and configuration.

“For many customers there is no longer a compelling business case for deploying an on-premise Hadoop cluster given the secure, flexible and highly cost effective platform for running MapR that AWS provides,” said John Schroeder, CEO and co-founder, MapR Technologies. “The combination of AWS infrastructure and MapR’s technology, support and management tools enables organizations to potentially lower their costs while increasing the flexibility of their data intensive applications.”


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