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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

Playing with Avro using Schema Registry

Schema Registry allows you to store Avro schemas for both producers and consumers. It also provides a RESTful interface for accessing this schema. It stores all the versions of Avro schema, and each schema version is assigned a schema ID.

When the producer sends a record to Kafka topic using Avro Serialization, it does not send an entire schema, instead, it sends the schema ID and record. The Avro serializer keeps all the versions of the schema in cache and stores data with the schemas matching the schema ID.

The consumer also uses the schema ID to read records from Kafka topic, wherein the Avro deserializer uses the schema ID to deserialize the record.

The Schema Registry also supports schema compatibility where we can modify the setting of schema compatibility to support forward and backward compatibility.

Here is an example of Avro...

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