Fast Data Processing with Spark 2 - Third Edition

Krishna Sankar

• Éditeur: Packt Publishing
• Année: 2016
• ISBN:
  9781785882968
• Édition: 3
• Sujet: Computer Science
• Sous-sujet: Data Processing

Learn how to use Spark to process big data at speed and scale for sharper analytics. Put the principles into practice for faster, slicker big data projects.About This Book• A quick way to get started with Spark – and reap the rewards• From analytics to engineering your big data architecture, we've got it covered• Bring your Scala and Java knowledge – and put it to work on new and exciting problemsWho This Book Is ForThis book is for developers with little to no knowledge of Spark, but with a background in Scala/Java programming. It's recommended that you have experience in dealing and working with big data and a strong interest in data science.What You Will Learn• Install and set up Spark in your cluster• Prototype distributed applications with Spark's interactive shell• Perform data wrangling using the new DataFrame APIs• Get to know the different ways to interact with Spark's distributed representation of data (RDDs)• Query Spark with a SQL-like query syntax• See how Spark works with big data• Implement machine learning systems with highly scalable algorithms• Use R, the popular statistical language, to work with Spark• Apply interesting graph algorithms and graph processing with GraphXIn DetailWhen people want a way to process big data at speed, Spark is invariably the solution. With its ease of development (in comparison to the relative complexity of Hadoop), it's unsurprising that it's becoming popular with data analysts and engineers everywhere. Beginning with the fundamentals, we'll show you how to get set up with Spark with minimum fuss. You'll then get to grips with some simple APIs before investigating machine learning and graph processing – throughout we'll make sure you know exactly how to apply your knowledge. You will also learn how to use the Spark shell, how to load data before finding out how to build and run your own Spark applications. Discover how to manipulate your RDD and get stuck into a range of DataFrame APIs. As if that's not enough, you'll also learn some useful Machine Learning algorithms with the help of Spark MLlib and integrating Spark with R. We'll also make sure you're confident and prepared for graph processing, as you learn more about the GraphX API.Style and approachThis book is a basic, step-by-step tutorial that will help you take advantage of all that Spark has to offer.

Fast Data Processing with Spark 2 Third Edition

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Fast Data Processing with Spark 2 Third Edition

Copyright © 2016 Packt Publishing

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First published: October 2013

Second edition: March 2015

Third edition: October 2016

Production reference: 1141016

Published by Packt Publishing Ltd.

Livery Place

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Birmingham B3 2PB, UK.

ISBN 978-1-78588-927-1

www.packtpub.com

Credits

Author Krishna Sankar	Copy Editor Safis Editing
Reviewers Sumit Pal Alexis Roos	Project Coordinator Suzzane Coutinho
Commissioning Editor Akram Hussain	Proofreader Safis Editing
Acquisition Editor Tushar Gupta	Indexer Tejal Daruwale Soni
Content Development Editor Nikhil Borkar	Graphics Kirk D'Penha
Technical Editor Madhunikita Sunil Chindarkar	Production Coordinator Melwyn D'sa

About the Author

Krishna Sankar is a Senior Specialist—AI Data Scientist with Volvo Cars focusing on Autonomous Vehicles. His earlier stints include Chief Data Scientist at http://cadenttech.tv/, Principal Architect/Data Scientist at Tata America Intl. Corp., Director of Data Science at a bioinformatics startup, and as a Distinguished Engineer at Cisco. He has been speaking at various conferences including ML tutorials at Strata SJC and London 2016, Spark Summit [goo.gl/ab30lD], Strata-Spark Camp, OSCON, PyCon, and PyData, writes about Robots Rules of Order [goo.gl/5yyRv6], Big Data Analytics—Best of the Worst [goo.gl/ImWCaz], predicting NFL, Spark [http://goo.gl/E4kqMD], Data Science [http://goo.gl/9pyJMH], Machine Learning [http://goo.gl/SXF53n], Social Media Analysis [http://goo.gl/D9YpVQ] as well as has been a guest lecturer at the Naval Postgraduate School. His occasional blogs can be found at https://doubleclix.wordpress.com/. His other passion is flying drones (working towards Drone Pilot License (FAA UAS Pilot) and Lego Robotics—you will find him at the St.Louis FLL World Competition as Robots Design Judge.

My first thanks goes to you, the reader, who is taking time to understand the technologies that Apache Spark brings to computation and to the developers of the Spark platform. The book reviewers Sumit and Alexis did a wonderful and thorough job morphing my rough materials into correct readable prose. This book is the result of dedicated work by many at Packt, notably Nikhil Borkar, the Content Development Editor, who deserves all the credit. Madhunikita, as always, has been the guiding force behind the hard work to bring the materials together, in more than one way. On a personal note, my bosses at Volvo viz. Petter Horling, Vedad Cajic, Andreas Wallin, and Mats Gustafsson are a constant source of guidance and insights. And of course, my spouse Usha and son Kaushik always have an encouraging word; special thanks to Usha’s father Mr.Natarajan, whose wisdom we all rely upon, and my late mom for her kindness.

About the Reviewers

Sumit Pal has more than 22 years of experience in the software industry in various roles spanning companies from startups to enterprises. He is a big data, visualization, and data science consultant and a software architect and big data enthusiast and builds end-to-end data-driven analytic systems. He has worked for Microsoft (SQL server development team), Oracle (OLAP development team), and Verizon (big data analytics team) in a career spanning 22 years. Currently, he works for multiple clients, advising them on their data architectures and big data solutions and does hands on coding with Spark, Scala, Java, and Python. He has extensive experience in building scalable systems across the stack from middle tier, data tier to visualization for analytics applications, using big data and NoSQL databases.

Sumit has deep expertise in DataBase Internals, Data Warehouses, Dimensional Modeling, and Data Science with Java and Python and SQL. Sumit started his career being part of SQL Server development team at Microsoft in 1996-97 and then as a Core Server Engineer for Oracle at their OLAP development team in Burlington, MA. Sumit has also worked at Verizon as an Associate Director for big data architecture, where he strategized, managed, architected, and developed platforms and solutions for analytics and machine learning applications. He has also served as Chief Architect at ModelN/LeapfrogRX (2006-2013) where he architected the middle tier core Analytics Platform with open source OLAP engine (Mondrian) on J2EE and solved some complex Dimensional ETL, modeling, and performance optimizati...