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High Performance Spark (Best Practices for Scaling and Optimizing Apache Spark)

List Price: $49.99
SKU:
9781491943205
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  • Product Details

    Author:
    Holden Karau, Rachel Warren
    Format:
    Paperback
    Pages:
    356
    Publisher:
    O'Reilly Media (July 11, 2017)
    Language:
    English
    ISBN-13:
    9781491943205
    ISBN-10:
    1491943203
    Dimensions:
    7" x 9.19"
    Case Pack:
    11
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20260323163503-20260324.xml
    Folder:
    TWO RIVERS
    List Price:
    $49.99
    As low as:
    $42.99
    Publisher Identifier:
    P-PER
    Discount Code:
    C
    Country of Origin:
    United States
    Pub Discount:
    60
    Weight:
    19.2oz
    Imprint:
    O'Reilly Media
  • Overview

    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