Rajanarayanan Thottuvaikkatumana - Apache Spark 2 for Beginners
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- This book offers an easy introduction to the Spark framework published on the latest version of Apache Spark 2
- Perform efficient data processing, machine learning and graph processing using various Spark components
- A practical guide aimed at beginners to get them up and running with Spark
Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists.
This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data processing. After that, we take a look at Sparks stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application.
By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark.
What you will learn- Get to know the fundamentals of Spark 2 and the Spark programming model using Scala and Python
- Know how to use Spark SQL and DataFrames using Scala and Python
- Get an introduction to Spark programming using R
- Perform Spark data processing, charting, and plotting using Python
- Get acquainted with Spark stream processing using Scala and Python
- Be introduced to machine learning using Spark MLlib
- Get started with graph processing using the Spark GraphX
- Bring together all that youve learned and develop a complete Spark application
Rajanarayanan Thottuvaikkatumana, Raj, is a seasoned technologist with more than 23 years of software development experience at various multinational companies. He has lived and worked in India, Singapore, and the USA, and is presently based out of the UK. His experience includes architecting, designing, and developing software applications. He has worked on various technologies including major databases, application development platforms, web technologies, and big data technologies. Since 2000, he has been working mainly in Java related technologies, and does heavy-duty server-side programming in Java and Scala. He has worked on very highly concurrent, highly distributed, and high transaction volume systems. Currently he is building a next generation Hadoop YARN-based data processing platform and an application suite built with Spark using Scala.
Raj holds one masters degree in Mathematics, one masters degree in Computer Information Systems and has many certifications in ITIL and cloud computing to his credit. Raj is the author of Cassandra Design Patterns - Second Edition, published by Packt.
When not working on the assignments his day job demands, Raj is an avid listener to classical music and watches a lot of tennis.
Table of Contents- Spark Fundamentals
- Spark Programming Model
- Spark SQL
- Spark Programming with R
- Spark Data Analysis with Python
- Spark Stream Processing
- Spark Machine Learning
- Spark Graph Processing
- Designing Spark Applications
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Copyright 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: September 2016
Production reference: 1260916
Published by Packt Publishing Ltd.
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Birmingham
B3 2PB, UK.
ISBN 978-1-78588-500-6
www.packtpub.com
Author Rajanarayanan Thottuvaikkatumana | Copy Editor Safis editing |
Reviewer Kornel Skakowski | Project Coordinator Devanshi Doshi |
Acquisition Editor Tushar Gupta | Proofreader Safis Editing |
Content Development Editor Samantha Gonsalves | Indexer Rekha Nair |
Technical Editor Jayesh Sonawane | Graphics Jason Monteiro |
Production Coordinator Aparna Bhagat |
Rajanarayanan Thottuvaikkatumana , Raj, is a seasoned technologist with more than 23 years of software development experience at various multinational companies. He has lived and worked in India, Singapore, and the USA, and is presently based out of the UK. His experience includes architecting, designing, and developing software applications. He has worked on various technologies including major databases, application development platforms, web technologies, and big data technologies. Since 2000, he has been working mainly in Java related technologies, and does heavy-duty server-side programming in Java and Scala. He has worked on very highly concurrent, highly distributed, and high transaction volume systems. Currently he is building a next generation Hadoop YARN-based data processing platform and an application suite built with Spark using Scala.
Raj holds one master's degree in Mathematics, one master's degree in Computer Information Systems and has many certifications in ITIL and cloud computing to his credit. Raj is the author of Cassandra Design Patterns - Second Edition , published by Packt.
When not working on the assignments his day job demands, Raj is an avid listener to classical music and watches a lot of tennis.
Kornel Skakowski has a solid academic and industrial background. For more than five years, he worked as an assistant at AGH University of Science and Technology in Krakow. In 2015, he obtained his Ph.D. in the subject of machine learning-based adaptation of SOA systems. He has cooperated with several companies on various projects concerning intelligent systems, machine learning and big data. Currently, he works as a big data developer for SAP SE.
He is a co-author of 19 papers concerning software engineering, SOA systems and machine learning. He also works as a reviewer for the American Journal of Software Engineering and Applications. He has participated in numerous European and national scientific projects. His research interests include machine learning, big data and software engineering.
He is author of the book Data Lake Development for Big Data .
I would like to kindly thank my family, my relatives and my friends for their endless patience and support during reviewing this book. I would also like to express my special gratitude to my girlfriend Ania, for her understanding us missing time together.
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Dedicating this book to the countless volunteers who worked tirelessly to build high production-quality open source software products. Without them I wouldn't have written this book.
The data processing framework named Spark was first built to prove that, by re-using the data sets across a number of iterations, it provided value where Hadoop MapReduce jobs performed poorly. The research paper Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center talks about the philosophy behind the design of Spark. A very simplistic reference implementation built to test Mesos by the University of California Berkeley researchers has grown far and beyond to become a full blown data processing framework later became one of the most active Apache projects. It is designed from the ground up to do distributed data processing on clusters such as Hadoop, Mesos, and in standalone mode. Spark is a JVM-based data processing framework and hence it works on most operating systems that support JVM-based applications. Spark is widely installed on UNIX and Mac OS X, platforms and Windows adoption is increasing.
Spark provides a unified programming model using the programming languages Scala, Java, Python and R. In other words, irrespective of the language used to program Spark applications, the API remains almost the same in all the languages. In this way, organizations can adopt Spark and develop applications in their programming language of choice. This also enables fast porting of Spark applications from one language to another without much effort, if there is a need. Most of Spark is developed using Scala and because of that the Spark programming model inherently supports functional programming principles. The most basic Spark data abstraction is the resilient distributed data set (RDD), based on which all the other libraries are built. The RDD-based Spark programming model is the lowest level where developers can build data processing applications.
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