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Spark: Big Data Cluster Computing in Production,Used
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Productiontargeted Spark guidance with realworld use casesSpark: Big Data Cluster Computing in Production goes beyond general Spark overviews to provide targeted guidance toward using lightningfast bigdata clustering in production. Written by an expert team wellknown in the big data community, this book walks you through the challenges in moving from proofofconcept or demo Spark applications to live Spark in production. Real use cases provide deep insight into common problems, limitations, challenges, and opportunities, while expert tips and tricks help you get the most out of Spark performance. Coverage includes Spark SQL, Tachyon, Kerberos, ML Lib, YARN, and Mesos, with clear, actionable guidance on resource scheduling, db connectors, streaming, security, and much more.Spark has become the tool of choice for many Big Data problems, with more active contributors than any other Apache Software project. General introductory books abound, but this book is the first to provide deep insight and realworld advice on using Spark in production. Specific guidance, expert tips, and invaluable foresight make this guide an incredibly useful resource for real production settings. Review Spark hardware requirements and estimate cluster size Gain insight from realworld production use cases Tighten security, schedule resources, and finetune performance Overcome common problems encountered using Spark in productionSpark works with other big data tools including MapReduce and Hadoop, and uses languages you already know like Java, Scala, Python, and R. Lightning speed makes Spark too good to pass up, but understanding limitations and challenges in advance goes a long way toward easing actual production implementation. Spark: Big Data Cluster Computing in Production tells you everything you need to know, with realworld production insight and expert guidance, tips, and tricks.
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