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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

Best practices

Hopefully, at this juncture, you are very well aware of Kafka Producer APIs, their internal working, and common patterns of publishing messages to different Kafka topics. This section covers some of the best practices associated with Kafka producers. These best practices will help you in making some of the design decisions for the producer component.

Let's go through some of the most common best practices to design a good producer application:

  • Data validation: One of the aspects that is usually forgotten while writing a producer system is to perform basic data validation tests on data that is to be written on the Kafka cluster. Some such examples could be conformity to schema, not null values for Key fields, and so on. By not doing data validation, you are risking breaking downstream consumer applications and affecting the load balancing of brokers as data...
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