In this practical book, four Cloudera data scientists present a set of selfcontained patterns for performing largescale data analysis with Spark. The authors bring Spark, statistical methods, and realworld 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—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entrylevel understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find 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 traffic with Kmeans clustering

Understanding Wikipedia with Latent Semantic Analysis

Analyzing cooccurrence networks with GraphX

Geospatial and temporal data analysis on the New York City Taxi Trips data

Estimating financial risk through Monte Carlo simulation

Analyzing genomics data and the BDG project

Analyzing neuroimaging data with PySpark and Thunder