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Apache Spark for Data Science Cookbook
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Apache Spark for Data Science Cookbook
Copyright Ā© 2016 Packt Publishing
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First published: December 2016
Production reference: 1161216
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-78588-010-0
www.packtpub.com
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Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey dean's work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.
First, I would like to thank the Packt Publishing team for providing a great opportunity for me to take part in this exciting journey and would like to express my special thanks and gratitude to my family, friends and colleagues who has been very supportive and helped me in finishing this project within time.
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Roberto Corizzo is a PhD student at the Department of Computer Science, University of Bari, Italy. His research interests include Big Data analytics, data mining, and predictive modeling techniques for sensor networks. He has been involved as technical reviewer for Packt's Learning Hadoop 2 and Learning Python Web Penetration Testing video courses.
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In recent years, the volume of data being collected, stored, and analyzed has exploded, in particular in relation to the activity on the Web and mobile devices, as well as data from the physical world collected via sensor networks. While previously large-scale data storage, processing, analysis, and modeling was the domain of the largest institutions such as Google, Yahoo!, Facebook, and Twitter, increasingly, many organizations are being faced with the challenge of how to handle a massive amount of data.
With the advent of big data, extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.
The objective of this book is to get the audience the flavor of challenges in data science and addressing them with a variety of analytical tools on a distributed system such as Spark (apt for iterative algorithms), which offers in-memory processing and more flexible for data analysis at scale. This book introduces readers to the fundamentals of Spark and helps them learn the concepts with code examples. It also talks in brief about data mining, text mining, NLP, machine learning, and so on. The readers get to know how to solve real-world analytical problems with large datasets and are made aware of a very practical approach and code to use analytical tools that leverage the features of Spark.
Chapter 1, Big Data Analytics with Spark, introduces Scala, Python and R can be used for data analysis. It also details about Spark programming model, API will be introduced, shows how to install, set up a development environment for the Spark framework and run jobs in distributed mode. I will also show working with DataFrames and Streaming computation models.
Chapter 2, Tricky Statistics with Spark, shows how to apply various statistical measures such as generating sample data, constructing frequency tables, summary and descriptive statistics on large datasets using Spark and Pandas
Chapter 3, Data Analysis with Spark, details how to apply common data exploration and preparation techniques such as univariate analysis, bivariate analysis, missing values treatment, identifying the outliers and techniques for variable transformation using Spark.
Chapter 4, Clustering, Classification and Regression, deals with creating models for regression, classification and clustering as well as shows how to utilize standard performance-evaluation methodologies for the machine learning models built.
Chapter 5, Working with Spark MLlib, provides an overview of Spark MLlib and ML pipelines and presents examples for implementing Naive Bayes classification, ...
