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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Over insightful 90 recipes to get lightning-fast analytics with Apache Spark
About This Book
- Use Apache Spark for data processing with these hands-on recipes
- Implement end-to-end, large-scale data analysis better than ever before
- Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data
Who This Book Is For
This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful.
What You Will Learn
- Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning.
- Solve real-world analytical problems with large data sets.
- Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale.
- Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package.
- Learn about numerical and scientific computing using NumPy and SciPy on Spark.
- Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models.
In Detail
Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease.
This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark's data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.
Style and approach
This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficulties of data science. This book outlines practical steps to produce powerful insights into Big Data through a recipe-based approach.
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Seitenzahl: 383
Veröffentlichungsjahr: 2016
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Table of Contents
Apache Spark for Data Science Cookbook
Apache Spark for Data Science Cookbook
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.
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First published: December 2016
Production reference: 1161216
Published by Packt Publishing Ltd.
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B3 2PB, UK.
ISBN 978-1-78588-010-0
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Credits
Author
Padma Priya Chitturi
Copy Editor
Safis Editing
Reviewer
Roberto Corizzo
Project Coordinator
Shweta H Birwatkar
Commissioning Editor
Akram Hussain
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Acquisition Editors
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Indexer
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About the Author
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.
About the Reviewer
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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Preface
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.
What this book covers
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, decision trees and recommendation systems.
Chapter 6, NLP with Spark, shows how to install NLTK, Anaconda and apply NLP tasks such as POS tagging, Named Entity Recognition, Chunker, Sentence Detector, Lemmatization using Core NLP and Stanford NLP over Spark.
Chapter 7, Working with Sparkling Water - H2O, details how to integrate H2O with Spark and shows applying various algorithms such as k-means, deep learning and SVM and also show developing applications –spam detection and crime detection with Sparkling Water.
Chapter 8, Data Visualization with Spark, show the integration of widely used visualization tools such as Zeppelin, Lightning Server and highly active Scala bindings (Bokeh-Scala) for visualizing large data sets.
Chapter 9, Deep Learning on Spark, shows how to implement deep learning algorithms such as RBM, CNN for learning MNIST, Feed-forward neural networks with the tools Deep Learning4j, TensorFlow using Spark.
Chapter 10, Working with SparkR, provides examples on creating distributed data frames in R, various operations that could be applied in SparkR and details on applying user-defined functions, SQL queries and machine learning in SparkR.
What you need for this book
Throughout this book, we assume that you have some basic experience with programming in Scala, Java, or Python and have some basic knowledge of machine learning, statistics, and data analysis.
Who this book is for
This book is intended for entry-level to intermediate data scientists, data analysts, engineers and practitioners who want to get acquainted with solving numerous data science problems using a distributed computing framework like Spark. The readers are expected to have knowledge on statistics, data science tools like R, Pandas and understanding on distributed systems (some exposure to Hadoop).
Sections
In this book, you will find several headings that appear frequently (Getting ready, How to do it, How it works, There's more, and See also).
To give clear instructions on how to complete a recipe, we use these sections as follows:
Getting ready
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
How to do it…
This section contains the steps required to follow the recipe.
How it works…
This section usually consists of a detailed explanation of what happened in the previous section.
There's more…
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
See also
This section provides helpful links to other useful information for the recipe.
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Downloading the example code
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Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
The code bundle for the book is also hosted on GitHub at https://github.com/ChitturiPadma/SparkforDataScienceCookbook. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
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Questions
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Chapter 1. Big Data Analytics with Spark
In this chapter, we will cover the components of Spark. You will learn them through the following recipes:
Introduction
Apache Spark is a general-purpose distributed computing engine for large-scale data processing. It is an open source initiative from AMPLab and donated to the Apache Software Foundation. It is one of the top-level projects under the Apache Software Foundation. Apache Spark offers a data abstraction called Resilient Distributed Datasets (RDDs) to analyze the data in parallel on top of a cluster of resources. The Apache Spark framework is an alternative to Hadoop MapReduce. It is up to 100X faster than MapReduce and offers the best APIs for iterative and expressive data processing. This project is written in Scala and it offers client APIs in Scala, Java, Python, and R.
