T�tulo: Beginning Apache Spark 2 : With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning Library
Editor: Apress
Fecha de publicaci�n: 2018
Idioma: Ingl�s
ISBN 10: 1484235789
ISBN 13: 9781484235782
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

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
How to 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.

Acerca del autor

Hien Luu has extensive experience in designing and building big data applications and scalable web-based applications. He is particularly passionate about the intersection between big data and machine learning. Hien enjoys working with open source software and has contributed to Apache Pig and Azkaban. Teaching is also one of his passions, and he serves as an instructor at the UCSC Silicon Valley Extension school teaching Apache Spark. He has given presentations at various conferences such a QCon SF, QCon London, Seattle Data Day, Hadoop Summit, and JavaOne.

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