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Beginning Apache Spark 2 eBook

With Resilient Distributed Datasets, Spark Sql, Structured Streaming And Spark Machine Learning Library

by Hien Luu
language: english
Publisher: Apress, August of 2018 ‧
52,99€
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Ebook for ADE
Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it.

Along the way, you’ll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you’ll learn the fundamentals of Spark ML for machine learning and much more. 

After you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications.  


What You Will Learn  
  • Understand Spark unified data processing platform
  • Howto run Spark in Spark Shell or Databricks 
  • Use and manipulate RDDs 
  • Deal with structured data using Spark SQL through its operations and advanced functions
  • Build real-time applications using Spark Structured Streaming
  • Develop intelligent applications with the Spark Machine Learning library

Who This Book Is For

Programmers and developers active in big data, Hadoop, and Java but who are new to the Apache Spark platform.  

Beginning Apache Spark 2

With Resilient Distributed Datasets, Spark Sql, Structured Streaming And Spark Machine Learning Library

by Hien Luu

Property Description
ISBN: 9781484235799
Publisher: Apress
Release Date: August of 2018
Language: English
Format: eBook
File Format and Compatibility:
Collection: Professional And Applied Computing
Categories: eBooks in English > Science > Mathematics
eBooks in English > Computing > Database
EAN: 9781484235799
Acessibilidade: Ver características de acessibilidade indicadas pelo editor