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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

Working with Trident

Throughout the book, we have portrayed Storm as a solution to be used as a high-performant, real-time stream computational tool. But, in reality, all real-time use cases are not actually real-time; they are an extension to real-time and use an amalgamation of micro batching. Let's give you some examples.

Let's say we want to know the names of the top five performing stocks. This data should reflect the stock performance over the last 10 minutes. We also want to know which is the most liked picture on Facebook for each 5-minute period.

There are numerous scenarios that require small unit batching over real-time streaming data and computations around the same; thus, the need for the extension of Storm.

Trident, like its predecessor, Storm, was also incepted at the Twitter technology house. At a high level, it's an extension and abstraction over the top of the Storm framework, with some additional capability for batching, stateful processing, and querying...

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