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DATASPARK

Advanced Analytics with Spark

ISBN: 978-1-491-91276-8

US $49.99 CAN $57.99

Twitter: @oreillymedia

facebook.com/oreilly

In this practical book, four Cloudera data scientists present a set of self-

contained patterns for performing large-scale data analysis with Spark. The

authors bring Spark, statistical methods, and real-world data sets together to

teach you how to approach analytics problems by example.

You’ll start with an introduction to Spark and its ecosystem, and then dive into

patterns that apply common techniques—classication, collaborative ltering,

and anomaly detection, among others—to elds such as genomics, security,

and nance. If you have an entry-level understanding of machine learning and

statistics, and you program in Java, Python, or Scala, you’ll nd these patterns

useful for working on your own data applications.

Patterns include:

■ Recommending music and the Audioscrobbler data set

■ Predicting forest cover with decision trees

■ Anomaly detection in network trac with K-means clustering

■ Understanding Wikipedia with Latent Semantic Analysis

■ Analyzing co-occurrence networks with GraphX

■ Geospatial and temporal data analysis on the New York City

Taxi Trips data

■ Estimating nancial risk through Monte Carlo simulation

■ Analyzing genomics data and the BDG project

■ Analyzing neuroimaging data with PySpark and Thunder

Sandy Ryza is a Senior Data Scientist at Cloudera and active contributor to the

Apache Spark project.

Uri Laserson is a Senior Data Scientist at Cloudera, where he focuses on Python

in the Hadoop ecosystem.

Sean Owen is Director of Data Science for EMEA at Cloudera, and a committer for

Apache Spark.

Josh Wills is Senior Director of Data Science at Cloudera and founder of the

Apache Crunch project.

Advanced Analytics with Spark

Ryza, Laserson,

Owen & Wills

Sandy Ryza, Uri Laserson,

Sean Owen & Josh Wills

Advanced

Analytics with

Spark

PATTERNS FOR LEARNING FROM DATA AT SCALE

Table of Contents

Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

Analyzing Big Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

The Challenges of Data Science 3

Introducing Apache Spark 4

About This Book 6

Introduction to Data Analysis with Scala and Spark. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Scala for Data Scientists 10

The Spark Programming Model 11

Record Linkage 11

Getting Started: The Spark Shell and SparkContext 13

Bringing Data from the Cluster to the Client 18

Shipping Code from the Client to the Cluster 22

Structuring Data with Tuples and Case Classes 23

Aggregations 28

Creating Histograms 29

Summary Statistics for Continuous Variables 30

Creating Reusable Code for Computing Summary Statistics 31

Simple Variable Selection and Scoring 36

Where to Go from Here 37

Recommending Music and the Audioscrobbler Data Set. . . . . . . . . . . . . . . . . . . . . . . . . . 39

Data Set 40

The Alternating Least Squares Recommender Algorithm 41

Preparing the Data 43

iii

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