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

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

• Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
• Learning Spark: Lightning-Fast Big Data Analysis
• High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
• Spark: The Definitive Guide: Big Data Processing Made Simple
• Programming in Scala: Updated for Scala 2.12
• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
• Functional Programming in Scala
• Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing