Advanced Analytics With Spark ─ Patterns for Learning from Data at Scale

In the second edition of this practical book, five 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 datasets 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. New chapters cover PySpark and MLlib, and Embarrassingly Parallel Python.

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 datasets
• Discover which machine learning tools make sense for particular problems
• Acquire code that can be adapted to many uses

Juliet Hougland is the Head of Data Science, Engineering at Cloudera. Juliet holds an MS in Applied Mathematics from University of Colorado, Boulder and graduated Phi Beta Kappa from Reed College with a BA in Math-Physics.

Uri Laserson is an Assistant Professor of Genetics at the Icahn School of Medicine at Mount Sinai, where he develops scalable technology for genomics and immunology using the Hadoop ecosystem.

Sean Owen is Director of Data Science at Cloudera. He is an ApacheSpark committer and PMC member, and was an Apache Mahout committer.

Sandy Ryza is a data science lead at Clover Health. Prior, he was a senior data scientist at Cloudera. He is an Apache Spark committer, Apache Hadoop PMC member, and founder of the Time Series for Spark project. He holds the Brown University computer science department's 2012 Twining award for "Most Chill".

Josh Wills is the Head of Data Engineering at Slack, the founder of the Apache Crunch project, and wrote a tweet about data scientists once.