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Building Data Streaming Applications with Apache Kafka

You're reading from   Building Data Streaming Applications with Apache Kafka Design, develop and streamline applications using Apache Kafka, Storm, Heron and Spark

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787283985
Length 278 pages
Edition 1st Edition
Tools
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Authors (2):
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Chanchal Singh Chanchal Singh
Author Profile Icon Chanchal Singh
Chanchal Singh
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Messaging Systems FREE CHAPTER 2. Introducing Kafka the Distributed Messaging Platform 3. Deep Dive into Kafka Producers 4. Deep Dive into Kafka Consumers 5. Building Spark Streaming Applications with Kafka 6. Building Storm Applications with Kafka 7. Using Kafka with Confluent Platform 8. Building ETL Pipelines Using Kafka 9. Building Streaming Applications Using Kafka Streams 10. Kafka Cluster Deployment 11. Using Kafka in Big Data Applications 12. Securing Kafka 13. Streaming Application Design Considerations

Latency and throughput

One of the fundamental features of any streaming application is to process inbound data from different sources and produce an outcome instantaneously. Latency and throughput are the important initial considerations for that desired feature. In other words, performance of any streaming application is measured in terms of latency and throughput.

The expectation from any streaming application is to produce outcomes as soon as possible and to handle a high rate of incoming streams. Both factors have an impact on the choice of technology and hardware capacity to be used in streaming solutions. Before we understand their impact in detail, let's first understand the meanings of both terms.

Latency is defined as the unit of time (in milliseconds) taken by the streaming application in processing an event or group of events and producing an output after the events...
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