Book Image

Big Data Analytics

By : Venkat Ankam
Book Image

Big Data Analytics

By: Venkat Ankam

Overview of this book

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.
Table of Contents (18 chapters)
Big Data Analytics
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface
Index

Acknowledgement

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.