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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

Big data and Kafka common usage patterns

In the big data world, Kafka can be used in multiple ways. One of the common usage patterns of Kafka is to use it as a streaming data platform. It supports storing streaming data from varied sources, and that data can later be processed in real time or in batch.

The following diagram shows a typical pattern for using Kafka as a streaming data platform:

Kafka as streaming data platform

The previous diagram depicts how Kafka can be used for storing events from a variety of data sources. Of course, the data ingestion mechanism would differ depending upon the type of data sources. However, once data is stored in Kafka topics, it can be used in data search engines, real-time processing, or alerting and even for batch processing.

Batch processing engines, such as Gobblin, read data from Kafka and use Hadoop MapReduce to store data in Hadoop...
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