Big Data Cloud Automation: Navigating through the Noise of Recommendations

Presented by

Joel Stewart, Pepperdata Vice President of Customer Success

About this talk

The ability to manage and optimize big data cloud performance has significantly improved through the implementation of automation and observability. This includes the capacity to provide recommendations to improve performance. However, understanding, trusting, and utilizing recommendations to optimize and improve big data performance remains a significant challenge. When bad data or the wrong data gets fed into big data algorithms, bad results can occur, and bad decisions can get made. With the scale of data and a myriad of overlapping dependencies and moving pieces, it’s not that surprising that infrastructure and application recommendations intended to help you improve performance often only add to the noise and hamper insight. Join Pepperdata Vice President of Customer Success Joel Stewart as he discusses how to navigate through the noise of big data cloud performance recommendations and more efficiently manage big data performance.

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Pepperdata is the Big Data performance company. Fortune 1000 enterprises depend on Pepperdata to manage and optimize the performance of Hadoop and Spark applications and infrastructure. Developers and IT Operations use Pepperdata soluions to diagnose and solve performance problems in production, increase infrastructure efficiencies, and maintain critical SLAs. Pepperdata automatically correlates performance issues between applications and operations, accelerates time to production, and increases infrastructure ROI. Pepperdata works with customer Big Data systems on-premises and in the cloud.