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