Justin Murray is a technical marketing architect within the core vSphere business unit at VMware in California, specializing in the big data field. He helps VMware’s partner companies and customers to get the best out of the virtualization platform when building big data systems for their business. Justin creates technical material on this subject and gives talks at public trade events on a regular basis.
This talk explains the reasons why virtualizing Spark, in-house or elsewhere, is a requirement in today’s fast-moving and experimental world of data science and data engineering. Different teams want to spin up a Spark cluster “on the fly” to carry out some research and quickly answer business questions. They are not concerned with the availability of the server hardware – or with what any other team might be doing on it at the time. Virtualization provides the means of working within your own sandbox to try out the new query or Machine Learning algorithm. Deep performance test results will be shown that demonstrate that Spark and ML programs perform equally well on virtual machines just like native implementations do. An early introduction is given to the best practices you should adhere to when you do this. If time allows, a short demo will be given of creating an ephemeral, single-purpose Spark cluster, running an ML application test program on that cluster, and bringing it down when finished.