Virtualizing Apache Spark and Machine Learning (Justin Murray)

172 views · Published 27 September 2018 · 30:30 · Indexed 26 October 2026

Channel: Databricks · 2018 · Science & Technology

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Justin Murray is a technical marketing architect within the core vSphere business unit at VMware in California, specializing in the big data field.

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.

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