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

About the Authors

Sandy Ryza develops algorithms for public transit at Remix. Prior, he was a senior data scientist at Cloudera and Clover Health. 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".

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 Apache Spark committer and PMC member, and was an Apache Mahout committer.

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.

"About this title" may belong to another edition of this title.

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