Ryza S. et al. Advanced Analytics with Spark: Patterns for Learning from Data at Scale

O’Reilly Media, 2015. — 275 p. — ISBN: 1491912766, 9781491912768Ryza S., Laserson U., Owen S., Wills J.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—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level 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 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 financial risk through Monte Carlo simulation
Analyzing genomics data and the BDG project
Analyzing neuroimaging data with PySpark and Thunder