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Mastering Spark with R (The Complete Guide to Large-Scale Analysis and Modeling)

List Price: $55.99
SKU:
9781492046370
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  • Product Details

    Author:
    Javier Luraschi, Kevin Kuo, Edgar Ruiz
    Format:
    Paperback
    Pages:
    293
    Publisher:
    O'Reilly Media (November 19, 2019)
    Language:
    English
    ISBN-13:
    9781492046370
    ISBN-10:
    149204637X
    Dimensions:
    7" x 9.19"
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20251023163248-20251023.xml
    Folder:
    TWO RIVERS
    List Price:
    $55.99
    As low as:
    $48.15
    Publisher Identifier:
    P-PER
    Discount Code:
    C
    Case Pack:
    13
    Country of Origin:
    United States
    Pub Discount:
    60
    Weight:
    16.8oz
    Imprint:
    O'Reilly Media
  • Overview

    If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.

    Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users.

    • Analyze, explore, transform, and visualize data in Apache Spark with R
    • Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows
    • Perform analysis and modeling across many machines using distributed computing techniques
    • Use large-scale data from multiple sources and different formats with ease from within Spark
    • Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale
    • Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions