Advanced Analytics with Spark: Patterns for Learning from Data at Scale

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9781491912768: Advanced Analytics with Spark: Patterns for Learning from Data at Scale
Vom Verlag:

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

Vom Verlag:

Apache Spark is emerging as one of the most popular technologies for performing analytics on huge datasets, and this practical guide shows you how to harness Spark’s power for approaching a variety of analytics problems. You’ll learn how to apply common techniques, such as classification, clustering, collaborative filtering, anomaly detection, dimensionality reduction, and Monte Carlo simulation to fields such as genomics, security, and finance.

Advanced Analytics with Spark supplies complete implementations that analyze large public datasets, and acts as an introduction to using these techniques and other best practices in Spark programming.

  • Become familiar with the Spark programming model and ecosystem
  • Learn general approaches in data science
  • Discover which machine learning tools make sense for particular problems
  • Acquire code from GitHub that can be adapted to many uses

This book will interest both data science professionals and aspiring data scientists, students studying learning techniques for analyzing large datasets, and scientists interested in using Spark as a research tool.

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