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Big Data Analytics

You're reading from   Big Data Analytics Real time analytics using Apache Spark and Hadoop

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785884696
Length 326 pages
Edition 1st Edition
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Author (1):
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Venkat Ankam Venkat Ankam
Author Profile Icon Venkat Ankam
Venkat Ankam
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Table of Contents (12) Chapters Close

Preface 1. Big Data Analytics at a 10,000-Foot View 2. Getting Started with Apache Hadoop and Apache Spark FREE CHAPTER 3. Deep Dive into Apache Spark 4. Big Data Analytics with Spark SQL, DataFrames, and Datasets 5. Real-Time Analytics with Spark Streaming and Structured Streaming 6. Notebooks and Dataflows with Spark and Hadoop 7. Machine Learning with Spark and Hadoop 8. Building Recommendation Systems with Spark and Mahout 9. Graph Analytics with GraphX 10. Interactive Analytics with SparkR Index

Introducing real-time processing

Big Data is generally ingested in real-time and the value of Big Data must be extracted on its arrival to make business decisions in real-time or near real-time, for example, fraud detection in financial transaction streams to accept or reject a transaction.

But, what is real-time and near real-time processing? The meaning of real-time or near real-time can vary from business to business and there is no standard definition for this. According to me, real-time means processing at the speed of a business. For a financial institution doing fraud detection, real-time means milliseconds for them. For a retail company doing click-stream analytics, real-time means seconds.

There are really only two paradigms for data processing: batch and real-time. Batch processing applications fundamentally provide high-latency, while real-time applications provide low latency. So, processing a few terabytes of data all at once will not be finished in a second. Real-time processing...

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