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. Youall start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniquesaclassification, collaborative filtering, and anomaly detection among othersato 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, youall 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... IMDB graph was built from actors who had appeared in the same movies, the Mac OS 9 network referred to header files that were co-included in the same source files in the OS 9 operating system source code, and .edu sites refers to sites inanbsp;...
|Title||:||Advanced Analytics with Spark|
|Author||:||Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills|
|Publisher||:||"O'Reilly Media, Inc." - 2015-04-02|