Mastering Apache Spark


Автор: Mike Frampton
Издательство: 

Год: 2015
Страниц: 318
Язык: английский
Формат: PDF
ISBN: 1783987146

 Аннотация:

  •  Explore the integration of Apache Spark with third party applications such as H20, Databricks and Titan
  • Evaluate how Cassandra and Hbase can be used for storage
  • An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities

Who This Book Is For (для кого написана эта книга)

If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected.

What You Will Learn (чему вы научитесь, изучая данную книгу):

  • Extend the tools available for processing and storage
  • Examine clustering and classification using MLlib
  • Discover Spark stream processing via Flume, HDFS
  • Create a schema in Spark SQL, and learn how a Spark schema can be populated with data
  • Study Spark based graph processing using Spark GraphX
  • Combine Spark with H20 and deep learning and learn why it is useful
  • Evaluate how graph storage works with Apache Spark, Titan, HBase and Cassandra
  • Use Apache Spark in the cloud with Databricks and AWS

In Detail (подробное описание книги)

Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations.

This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment.

Style and approach (стиль и подход к изложению материала)

This book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.

 

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