New Pepperdata Insights Service Reduces Troubleshooting Efforts by 90% and Streamlines Big Data Operations
CUPERTINO, California—September 13, 2016 – Pepperdata recently surveyed over 100 production Hadoop users, and fast and effective troubleshooting was reported as a top operations challenge, second only to a lack of expertise or skills. Based on this customer need for improved troubleshooting tools, the company is announcing the Pepperdata Insights Service, a 30-day license for the most comprehensive Hadoop troubleshooting technology available today, along with performance troubleshooting expertise. Pepperdata’s granular cluster diagnostics help operations teams reduce troubleshooting by up to 90 percent and solve their most challenging performance problems.
Even the most advanced Hadoop users encounter blind spots in the daily operations of their Hadoop clusters and frequently find themselves experiencing slowdowns or failures without being able to identify the root cause. The new service provides an opportunity to use Pepperdata software for one month and experience firsthand how fine-grained cluster metrics can help solve troubleshooting issues faster than ever before. Customers that purchase Pepperdata Insights Service receive:
- A 30-day license to the software, with full product support and no limitations on cluster size
- Two hours of performance troubleshooting support, to diagnose performance problems.
- An easy-to-use dashboard that provides visibility into usage of CPU, memory, disk, and network by group, user, and job.
Once the 30-day subscription has ended, organizations receive a full diagnostic report of their cluster activity, accompanied by expert recommendations from Pepperdata on how to improve performance moving forward.
“Hadoop operations team are struggling to troubleshoot with existing Hadoop tools that only provide node level visibility,” said Ed Colonna, VP marketing and business development, Pepperdata. “Pepperdata is the only solution that provides deep, granular visibility into hardware usage at the user, job and task level across the entire cluster to solve performance problems with minimal effort.”