Overcome the limitations of distributed computing with real-time intelligence
Date: This event took place live on March 01 2016
Presented by: Sean Suchter
Duration: Approximately 60 minutes.
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Distributed computing platforms such as Hadoop have created a unique class of performance problems. Issues such as resource contention, jobs running late, and the inability to troubleshoot prohibit organizations from realizing the full value of their investment. The existing tools on the market today are insufficient to address these performance gaps. Schedulers and monitoring tools are essential to get jobs on the cluster and to see what is happening (at the node level), but they offer no active control of jobs once they are running, and don’t go deep enough so you can understand what is happening at the job, user, or task level.
Most Hadoop admins have learned a set of “best practices” to address and mitigate this performance dilemma, such as manual tuning, cluster isolation, or adding new hardware, but most of these remedies are not sustainable long-term solutions. Join us for this webcast as we survey some of these “best practices” and offer up some new ways to address the performance gap. We’ll also tell you the warning signs to look out for, so you can assess the health and production readiness of your cluster.
In this webcast, we’ll examine:
This webcast is the first of four in a series exploring the Hadoop performance paradigm. In this series, we will discuss some specific limitations of distributed computing, and how to overcome them to increase ROI.
About Sean Suchter, CEO & Co-Founder—Pepperdata
Sean has been working with Hadoop and distributed systems for more than 15 years. He was the founding GM of Microsoft’s Silicon Valley Search Technology Center, where he led the integration of Facebook and Twitter content into Bing search. Prior to Microsoft, Sean managed the Yahoo Search Technology Team, the first production user of Hadoop. Sean joined Yahoo through the acquisition of Inktomi. He holds a B.S. in Engineering and Applied Science from Caltech.