Advanced Analytics with Spark (2 ed)

In the second edition of this practical guide, four Cloudera data scientists present a collection of self-contained patterns for performing large-scale data analysis with Spark. Combining Spark’s power with statistical methods and real-world datasets, the authors teach you how to approach analytics challenges through hands-on examples. Updated for Spark 2.1, this edition serves as both an introduction to these techniques and a guide to best practices in Spark programming.

You’ll begin with an overview of Spark and its ecosystem, 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 a basic understanding of machine learning and statistics and program in Java, Python, or Scala, you’ll find these patterns invaluable for building your own data applications.

With this book, you will:

• Master the Spark programming model
• Gain confidence navigating the Spark ecosystem
• Learn general approaches in data science
• Explore complete implementations that analyze large public datasets
• Identify which machine learning tools are best suited for specific problems
• Access reusable code adaptable to a wide range of applications