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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

Building Spark Streaming Applications with Kafka

We have gone through all the components of Apache Kafka and different APIs that can be used to develop an application which can use Kafka. In the previous chapter, we learned about Kafka producer, brokers, and Kafka consumers, and different concepts related to best practices for using Kafka as a messaging system.

In this chapter, we will cover Apache Spark, which is distributed in memory processing engines and then we will walk through Spark Streaming concepts and how we can integrate Apache Kafka with Spark.

In short, we will cover the following topics:

  • Introduction to Spark
  • Internals of Spark such as RDD
  • Spark Streaming
  • Receiver-based approach (Spark-Kafka integration)
  • Direct approach (Spark-Kafka integration)
  • Use case (Log processing)
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