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Learning Real-time Analytics with Storm and Cassandra

You're reading from   Learning Real-time Analytics with Storm and Cassandra Solve real-time analytics problems effectively using Storm and Cassandra

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Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781784395490
Length 220 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 (14) Chapters Close

Preface 1. Let's Understand Storm FREE CHAPTER 2. Getting Started with Your First Topology 3. Understanding Storm Internals by Examples 4. Storm in a Clustered Mode 5. Storm High Availability and Failover 6. Adding NoSQL Persistence to Storm 7. Cassandra Partitioning, High Availability, and Consistency 8. Cassandra Management and Maintenance 9. Storm Management and Maintenance 10. Advance Concepts in Storm 11. Distributed Cache and CEP with Storm A. Quiz Answers Index

Introduction to the complex event processing engine

There are two terms that are generally used in conjunction; they are Complex Event Processing (CEP) and Event Stream Processing (ESP).

Well, in theory, these are part of a technical paradigm that allow us to build applications with dramatic, real-time analytics over streaming data. They let us process incoming events at a very fast rate and execute SQL-like queries on top of the stream of events to generate real-time histograms. We can assume that CEP is an inversion of traditional databases. In the case of traditional DBMS and RDBMS, we have data stored, and then we run SQL queries over them to arrive at results, while in the case of CEPs, we have the queries predefined or stored and we run the data through them. We can envision this with an example; let's say I am running a department store and I would like to know the highest selling item in the last one hour. So if you look here, the query we are about to execute is pretty fixed...

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