Pepperdata Releases Self-Service Platform for Amazon Elastic MapReduce

Pepperdata unveiled a new offering that enables customers of Amazon Elastic MapReduce (EMR) to gain granular visibility into their clusters’ run time performance.

Even after an Amazon EMR cluster has completed its work and is terminated, users will be able to access fine-grained monitoring data that allows customers to view a run and analyze it, as well as compare it with historical data to improve future performance.

Pepperdata’s granular analysis of runs – based on over 300 metrics, including CPU, memory, unused capacity, and job duration – helps DevOps teams optimize workloads and decrease run times caused by code inefficiencies, according to the company.

In addition to the self-service option for Amazon EMR, Pepperdata is offering beta availability of Adaptive Scaling for Amazon EMR.

With Adaptive Scaling, customers can specify a time or cost budget for job completion and Pepperdata will automatically purchase instances with Amazon EMR that will elastically grow or shrink as needed to meet these criteria.

Future plans call for the expansion of Adaptive Scaling and more, according to Sean Suchter, CEO and cofounder of Pepperdata.

“We’re trying to make it a no brainer for as many of EMR users as we can at the free price point that we’re starting with pretty much should be something everyone who’s using EMR ought to use,” Suchter said.

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