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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

View More author details

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

Kafka Connect common use cases

You have learned about Kafka Connect in detail. We know Kafka Connect is used for copying data in and out of Kafka.

Let's understand a few common use cases of Kafka Connect:

Copying data to HDFS: User wants to copy data from Kafka topics to HDFS for various reasons. A few want to copy it to HDFS just to take a backup of the history data, others may want to copy it to HDFS for batch processing. However, there are already many open source tools available, such as Camus, Gobblin, Flume, and so on, but maintaining, installing, and running these jobs takes more effort than what Kafka Connect provides. Kafka Connect copies data from topics in parallel and is capable of scaling up more if required.
Replication: Replicating Kafka topics from one cluster to another cluster is also a popular feature offered by Kafka Connect. You may want to replicate...

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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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