I didn’t grow up celebrating Christmas, but every time I watched Chevy Chase, as Clark Griswold in National Lampoon’s Christmas Vacation, trying to unravel his Christmas lights I could feel his pain. For those who had to put up lights, you might remember how time-consuming it was to unwrap those light. But that wasn’t the biggest problem. No the biggest issue was troubleshooting why one or more lights were out. It could be an issue with the socket, the wiring, or just one light causing a section of good lights to be out. Figuring out the root cause of the problem was a trial-and-error process that was very frustrating.
Today’s approach to diagnose and resolve performance issues of big data systems is just like dealing with those pesty Christmas lights. Current performance monitoring and management tools don’t pin point the root cause of problems or how they affect other systems or components running across a big data platform. As a result, troubleshooting and resolving performance issues, like rogue users and jobs impacting cluster performance, missed SLAs, stuck jobs, failed queries, or not understanding cluster usage and application performance, is very time consuming and cannot scale to support big data applications in a production deployment.
There is a better way.
You can watch this webinar to learn about how companies have implemented a solution to do Application Performance Management that can automatically detect, diagnose and resolve issues to improve productivity, guarantee reliability, and reduce costs for running big data applications in any environment– on-premises, in the cloud, or hybrid.