In the second edition of 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. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming.

You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques--including classification, clustering, collaborative filtering, and anomaly detection--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 the book's patterns useful for working on your own data applications.

With this book, you will:

• Familiarize yourself with the Spark programming model
• Become comfortable within the Spark ecosystem
• Learn general approaches in data science
• Examine complete implementations that analyze large public data sets
• Discover which machine learning tools make sense for particular problems
• Acquire code that can be adapted to many uses