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
