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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
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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

Data sources

One of the fundamental requirements for any streaming application is that the sources of data should have the ability to produce unbound data in terms of streams. Streaming systems are built for unbound data streams. If source systems have the support for such kinds of data streams, then streaming solutions are the way to go, but if they do not have support for data streams, then either you must build or use prebuilt custom components that build data streams out of those data sources or go for batch-oriented non-streaming-based solutions.

Either way, the key takeaway is that streaming solutions should have data stream producing data sources. This is one of the key design decisions in any streaming application. Any streaming solution or design should ensure that continuous unbound data streams are input to your stream processing engines.
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