Advanced analytics with Spark. Patterns for learning from data at scale
By: Ryza, Sandy [aut].
Contributor(s): Laserson, Uri [aut] | Owen, Sean [aut] | Wills, Josh [aut].
Material type:
BookPublisher: Beijing O'Reilly [2017]Edition: Second edition.Description: XII, 264 S. Ill.ISBN: 1-4919-7295-5; 978-1-4919-7295-3.Subject(s): Data Analyses & Machine Learning | Big data | Data mining -- Computer programsSummary: The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by presenting examples and a set of self-contained patterns for performing large-scale data analysis with Spark. 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.
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The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by presenting examples and a set of self-contained patterns for performing large-scale data analysis with Spark. 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.