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Learn How to Tame Big Data

Explore Lessons Learned and Best Practices for Optimizing Hadoop and NoSQL Deployments

Compuware Corporation, the technology performance company delivering a new generation of application performance management (APM), has announced a webcast that will examine best practices and lessons learned optimizing Hadoop and NoSQL deployments.

Title: "Learn How to Tame Big Data"

When: Thursday, February 27, 2014

Time: 2 p.m. Eastern Time

Presenter: Michael Villiger, Technical Evangelist, Compuware APM

Why: With all of the benefits big data offers, pitfalls still exist including slow Hadoop jobs, problems scaling your cluster efficiently or relying on war rooms to find the root cause of issues.

What: During this free webcast, attendees will learn how to:

  • find the root cause of slow and failed jobs;
  • identify bottlenecks without sifting through tons of log files; and
  • consistently monitor the health of internal and external data stores.

Speaker Bio: Michael Villiger has 15 years of experience in the software industry. His focus for the past decade has been infrastructure and application performance. He has previously managed large scale Hadoop and NoSQL deployments for a leading mapping and geo-location provider.

Compuware APM provides new generation of application performance management. Unlike traditional APM solutions that are heavy, difficult and reactive, Compuware APM is light, smart and proactive. Compuware APM is built to manage the complexity of today's most challenging modern applications including mobile, cloud, big data and SOA. Compuware APM optimizes and monitors tens of thousands of applications for more than 5,000 customers, large and small, around the globe. Through the lens of end-user experience, our customers enjoy faster performance, proactive problem resolution, accelerated time-to-market and reduced application management costs through smarter analytics, advanced APM automation and a unique performance lifecycle foundation.

More Stories By Elizabeth White

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