Spark Cookbook Rishi Yadav

ISBN:

Published: July 27th 2015

Kindle Edition

226 pages


Description

Spark Cookbook  by  Rishi Yadav

Spark Cookbook by Rishi Yadav
July 27th 2015 | Kindle Edition | PDF, EPUB, FB2, DjVu, audiobook, mp3, RTF | 226 pages | ISBN: | 10.72 Mb

Over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries About This Book Become an expert at graph processing using GraphX Use Apache Spark as your single big data compute platform and master itsMoreOver 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries About This Book Become an expert at graph processing using GraphXUse Apache Spark as your single big data compute platform and master its librariesLearn with recipes that can be run on a single machine as well as on a production cluster of thousands of machinesWho This Book Is For If you are a data engineer, an application developer, or a data scientist who would like to leverage the power of Apache Spark to get better insights from big data, then this is the book for you.What You Will Learn Install and configure Apache Spark with various cluster managersSet up development environmentsPerform interactive queries using Spark SQLGet to grips with real-time streaming analytics using Spark StreamingMaster supervised learning and unsupervised learning using MLlibBuild a recommendation engine using MLlibDevelop a set of common applications or project types, and solutions that solve complex big data problemsUse Apache Spark as your single big data compute platform and master its librariesIn Detail By introducing in-memory persistent storage, Apache Spark eliminates the need to store intermediate data in filesystems, thereby increasing processing speed by up to 100 times.This book will focus on how to analyze large and complex sets of data.

Starting with installing and configuring Apache Spark with various cluster managers, you will cover setting up development environments. You will then cover various recipes to perform interactive queries using Spark SQL and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will then focus on machine learning, including supervised learning, unsupervised learning, and recommendation engine algorithms.

After mastering graph processing using GraphX, you will cover various recipes for cluster optimization and troubleshooting.



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