In 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.
You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—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 these patterns useful for working on your own data applications.
Apache Spark is emerging as one of the most popular technologies for performing analytics on huge datasets, and this practical guide shows you how to harness Spark’s power for approaching a variety of analytics problems. You’ll learn how to apply common techniques, such as classification, clustering, collaborative filtering, anomaly detection, dimensionality reduction, and Monte Carlo simulation to fields such as genomics, security, and finance.
Advanced Analytics with Spark supplies complete implementations that analyze large public datasets, and acts as an introduction to using these techniques and other best practices in Spark programming.
This book will interest both data science professionals and aspiring data scientists, students studying learning techniques for analyzing large datasets, and scientists interested in using Spark as a research tool.
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