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

Product type Paperback

Published in Aug 2017

Publisher Packt

ISBN-13 9781787283985

Length 278 pages

Edition 1st Edition

Tools

Kafka

Concepts

Data Analysis

Authors (2):

Chanchal Singh

Manish Kumar

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Table of Contents (14) Chapters

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

Useful Kafka matrices

For useful monitoring and performance measures, we need to have certain matrices, and we will talk about those matrices in this section.

We will look into the matrices of Kafka cluster component in detail. The matrices are as follows:

Kafka producer matrices
Kafka broker matrices
Kafka consumer matrices

Producer matrices

Producers are responsible for producing data to Kafka topics. If the producer fails, the consumer will not have any new messages to consume and it will be left idle. The performance of the producer also plays an important role in achieving high throughput and latency. Let's look into a few important matrices of Kafka producer:

Response rate: The producer sends records to...

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Authors (2)

Singh

Contacted on 12/01/18 by Davis Anto

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Kumar

Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.

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