Frank Kane's Taming Big Data with Apache Spark and Python

by Frank Kane

Key FeaturesUnderstand how Spark can be distributed across computing clustersDevelop and run Spark jobs efficiently using PythonA hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with SparkBook DescriptionFrank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python.

Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses.

Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease.

What you will learnFind out how you can identify Big Data problems as Spark problemsInstall and run Apache Spark on your computer or on a clusterAnalyze large data sets across many CPUs using Spark's Resilient Distributed DatasetsImplement machine learning on Spark using the MLlib libraryProcess continuous streams of data in real time using the Spark streaming modulePerform complex network analysis using Spark's GraphX libraryUse Amazon's Elastic MapReduce service to run your Spark jobs on a clusterAbout the AuthorMy name is Frank Kane. I spent nine years at Amazon and IMDb, wrangling millions of customer ratings and customer transactions to produce things such as personalized recommendations for movies and products and "people who bought this also bought." I tell you, I wish we had Apache Spark back then, when I spent years trying to solve these problems there. I hold 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, I left to start my own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Table of ContentsGetting Started with SparkSpark Basics and Simple ExamplesAdvanced Examples of Spark ProgramsRunning Spark on a ClusterSparkSQL, Dataframes and DatasetsOther Spark Technologies and LibrariesWhere to Go From Here? - Learning More About Spark and Data Science

298 pages, Kindle Edition

Published June 30, 2017

About the author

Frank Kane, Brooklyn-born and a lifetime New Yorker, worked for many years in journalism and corporate public relations before shifting to fiction writing. At the time he was selling crime stories to the pulps he was also sustaining a career writing scripts for such radio shows as Gangbusters and The Shadow.

In addition to the Johnny Liddells, Kane wrote several suspense novels, some softcore erotica, and (under the pen name of Frank Boyd) "Johnny Staccato", a Gold Medal original paperback based on the short-lived noir television series, starring John Cassavetes, about a Greenwich Village bebop pianist turned private detective.

Reviews

[Arash Amani]

We are trying to implement a data lake on our network. During this work to launch Spark as a processing layer the book gave me a lot of ideas to do. I changed the examples to adapt to execute on cluster then I submit on our cluster.