9781491943205-1491943203-High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark

High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark

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Summary

High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark (ISBN-13: 9781491943205 and ISBN-10: 1491943203), written by authors Holden Karau, Rachel Warren, was published by O'Reilly Media in 2017. With an overall rating of 4.3 stars, it's a notable title among other Data Mining (Databases & Big Data, Data Warehousing, Data Processing, Internet, Groupware, & Telecommunications, Networking & Cloud Computing, Databases, Software, Web Design, Web Development & Design, Programming, Website Analytics, Java, Programming Languages, Internet & Social Media) books. You can easily purchase or rent High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark (Paperback, Used) from BooksRun, along with many other new and used Data Mining books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $4.49.

Description

Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources.

Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn how to make it sing.

With this book, you’ll explore:

  • How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure
  • The choice between data joins in Core Spark and Spark SQL
  • Techniques for getting the most out of standard RDD transformations
  • How to work around performance issues in Spark’s key/value pair paradigm
  • Writing high-performance Spark code without Scala or the JVM
  • How to test for functionality and performance when applying suggested improvements
  • Using Spark MLlib and Spark ML machine learning libraries
  • Spark’s Streaming components and external community packages
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