The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by presenting examples and a set of self-contained patterns for performing large-scale data analysis with Spark. 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

Analyzing big data -- Introduction to data analysis with Scala and Spark -- 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 trip data -- Estimating financial risk through Monte Carlo simulation -- Analyzing genomics data and the BDG project -- Analyzing neuroimaging data with PySpark and Thunder