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Real-Time Big Data Analytics

You're reading from   Real-Time Big Data Analytics Design, process, and analyze large sets of complex data in real time

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Product type Paperback
Published in Feb 2016
Publisher
ISBN-13 9781784391409
Length 326 pages
Edition 1st Edition
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Author (1):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
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Table of Contents (12) Chapters Close

Preface 1. Introducing the Big Data Technology Landscape and Analytics Platform FREE CHAPTER 2. Getting Acquainted with Storm 3. Processing Data with Storm 4. Introduction to Trident and Optimizing Storm Performance 5. Getting Acquainted with Kinesis 6. Getting Acquainted with Spark 7. Programming with RDDs 8. SQL Query Engine for Spark – Spark SQL 9. Analysis of Streaming Data Using Spark Streaming 10. Introducing Lambda Architecture Index

Understanding Spark transformations and actions


In this section, we will discuss and talk about various transformation and action operations provided by Spark RDD APIs. We will also discuss about the different forms of RDD APIs.

RDD or Resilient Distributed Dataset is the core component of Spark. All operations for performing transformations on the raw data are provided in the different RDD APIs. We discussed RDD APIs and its features in the Resilient distributed datasets (RDD) section in Chapter 6, Getting Acquainted with Spark, but it is important to mention again that there is no API for accessing the raw dataset. The data in Spark can only be accessed by various operations exposed by the RDD APIs. RDDs are immutable datasets, so any transformation applied on the raw dataset, generates a new RDD without any modifications to the datasets/RDD on which transformation operations are invoked. Transformations in RDD are lazy, which means invocation of any transformation is not applied immediately...

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