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Autor(en): 
  • Subhashini Chellappan
  • Dharanitharan Ganesan
  • Practical Apache Spark: Using the Scala API 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Dezember 2018  
    Genre:  EDV / Informatik 
     
    Apache Spark;Scala;Big Data;Machine Learning;Kafka;R / B / Big Data / Compilers & interpreters / Computer programming / Computer programming / software engineering / Open Source / Open Source Software
    ISBN:  9781484236512 
    EAN-Code: 
    9781484236512 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 254 mm / B 178 mm / D 16 mm 
    Gewicht:  566 gr 
    Seiten:  280 
    Illustration:  XVI, 280 p. 303 illus., schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Work with Apache Spark using Scala to deploy and set up single-node, multi-node, and high-availability clusters. This book discusses various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help of practical code snippets for each topic. Practical Apache Spark also covers the integration of Apache Spark with Kafka with examples. You'll follow a learn-to-do-by-yourself approach to learning - learn the concepts, practice the code snippets in Scala, and complete the assignments given to get an overall exposure.
    On completion, you'll have knowledge of the functional programming aspects of Scala, and hands-on expertise in various Spark components. You'll also become familiar with machine learning algorithms with real-time usage.
    What You Will Learn
    Discover the functional programming features of Scala Understand the completearchitecture of Spark and its components Integrate Apache Spark with Hive and Kafka Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries Work with different machine learning concepts and libraries using Spark's MLlib packages
    Who This Book Is For
    Developers and professionals who deal with batch and stream data processing.

      



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