Venkat Ankam - Big Data Analytics
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- Book:Big Data Analytics
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- Year:2016
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- This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools.
- Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR.
- Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall.
Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters.
It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark.
Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.
What you will learn- Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop
- Understand all the Hadoop and Spark ecosystem components
- Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx
- See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming
- Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall.
Venkat Ankam has over 18 years of IT experience and over 5 years in big data technologies, working with customers to design and develop scalable big data applications. Having worked with multiple clients globally, he has tremendous experience in big data analytics using Hadoop and Spark.
He is a Cloudera Certified Hadoop Developer and Administrator and also a Databricks Certified Spark Developer. He is the founder and presenter of a few Hadoop and Spark meetup groups globally and loves to share knowledge with the community.
Venkat has delivered hundreds of trainings, presentations, and white papers in the big data sphere. While this is his first attempt at writing a book, many more books are in the pipeline.
Table of Contents- Big Data Analytics at 10,000 foot view
- Getting Started with Apache Hadoop and Apache Spark
- Deep Dive into Apache Spark
- Big Data Analytics with Spark SQL, DataFrames, and Datasets
- Real-Time Analytics with Spark Streaming and Structured Streaming
- Notebooks and Dataflows with Spark and Hadoop
- Machine Learning with Spark and Hadoop
- Building Recommendation Systems with Spark and Mahout
- Graph Analytics with GraphX
- Interactive Analytics with SparkR
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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: 12309016
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78588-469-6
www.packtpub.com
Author
Venkat Ankam
Reviewers
Sreekanth Jella
De Witte Dieter
Commissioning Editor
Akram Hussain
Acquisition Editors
Ruchita Bhansali
Tushar Gupta
Content Development Editor
Sumeet Sawant
Technical Editor
Pranil Pathare
Copy Editors
Vikrant Phadke
Vibha Shukla
Project Coordinator
Shweta H Birwatkar
Proofreader
Safis Editing
Indexer
Mariammal Chettiyar
Graphics
Kirk D'Penha
Production Coordinator
Arvindkumar Gupta
Cover Work
Arvindkumar Gupta
Venkat Ankam has over 18 years of IT experience and over 5 years in big data technologies, working with customers to design and develop scalable big data applications. Having worked with multiple clients globally, he has tremendous experience in big data analytics using Hadoop and Spark.
He is a Cloudera Certified Hadoop Developer and Administrator and also a Databricks Certified Spark Developer. He is the founder and presenter of a few Hadoop and Spark meetup groups globally and loves to share knowledge with the community.
Venkat has delivered hundreds of trainings, presentations, and white papers in the big data sphere. While this is his first attempt at writing a book, many more books are in the pipeline.
I would like to thank Databricks for providing me with training in Spark in early 2014 and an opportunity to deepen my knowledge of Spark.
I would also like to thank Tyler Allbritton, principal architect, big data, cloud and analytics solutions at Tectonic, for providing me support in big data analytics projects and extending his support when writing this book.
Then, I would like to thank Mani Chhabra, CEO of Cloudwick, for encouraging me to write this book and providing the support I needed. Thanks to Arun Sirimalla, big data champion at Cloudwick, and Pranabh Kumar, big data architect at InsideView, who provided excellent support and inspiration to start meetups throughout India in 2011 to share knowledge of Hadoop and Spark.
Then I would like to thank Ashrith Mekala, solution architect at Cloudwick, for his technical consulting help.
This book started with a small discussion with Packt Publishing's acquisition editor Ruchita Bansali. I am really thankful to her for inspiring me to write this book. I am thankful to Kajal Thapar, content development editor at Packt Publishing, who then supported the entire journey of this book with great patience to refine it multiple times and get it to the finish line.
I would also like to thank Sumeet Sawant, Content Development Editor and Pranil Pathare, Technical Editor for their support in implementing Spark 2.0 changes.
I dedicate this book to my family and friends. Finally, this book would not have completed without the support from my wife, Srilatha, and my kids, Neha and Param, who cheered and encouraged me throughout the journey of this book.
Sreekanth Jella is a senior Hadoop and Spark developer with more than 11 years of IT industry development experience. He is a postgraduate from the University College of Engineering, Osmania University, with computer applications as major. He has worked in the USA, Turkey, and India and with clients such as AT&T, Cricket Communications, and Turk Telecom. Sreekanth has vast development experience with Java/J2EE technologies and web technologies as well. He is tech savvy and passionate about programming. In his words, " Coding is an art and code is fun " 
.
De Witte Dieter received his master's degree in civil engineering (applied physics) from Ghent University in 2008. During his master's, he became really interested in designing algorithms to tackle complex problems.
In April 2010, he was recruited as the first bioinformatics PhD student at IBCN-iMinds. Together with his colleagues, he designed high-performance algorithms in the area of DNA sequence analysis using Hadoop and MPI. Apart from developing and designing algorithms, an important part of the job was data mining, for which he mainly used Matlab. Dieter was also involved in teaching activities around Java/Matlab to first-year bachelor of engineering students.
From May 2014 onwards, he has been working as a big data scientist for Archimiddle (Cronos group). He worked on a big data project with Telenet, part of Liberty Global. Working in a Hadoop production environment together with a talented big data team, he considered it really rewarding and it made him confident in using the Cloudera Hadoop stack. Apart from consulting, he also conducted workshops and presentations on Hadoop and machine learning.
In December 2014, Dieter joined iMinds Data Science Lab, where he was responsible for research activities and consultancy with respect to big data analytics. He is currently teaching a course on big data science to master's students in computer science and statistics and doing consultancy on scalable semantic query systems.
I would like to thank iMinds Data Science Lab for all the opportunities and challenges they offer me.
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