Apache Kafka Quick Start Guide

By : Estrada

3.5 (2)

Buy this Book

Apache Kafka Quick Start Guide

3.5 (2)

By: Estrada

Buy this Book

Overview of this book

Apache Kafka is a great open source platform for handling your real-time data pipeline to ensure high-speed filtering and pattern matching on the ?y. In this book, you will learn how to use Apache Kafka for efficient processing of distributed applications and will get familiar with solving everyday problems in fast data and processing pipelines. This book focuses on programming rather than the configuration management of Kafka clusters or DevOps. It starts off with the installation and setting up the development environment, before quickly moving on to performing fundamental messaging operations such as validation and enrichment. Here you will learn about message composition with pure Kafka API and Kafka Streams. You will look into the transformation of messages in different formats, such asext, binary, XML, JSON, and AVRO. Next, you will learn how to expose the schemas contained in Kafka with the Schema Registry. You will then learn how to work with all relevant connectors with Kafka Connect. While working with Kafka Streams, you will perform various interesting operations on streams, such as windowing, joins, and aggregations. Finally, through KSQL, you will learn how to retrieve, insert, modify, and delete data streams, and how to manipulate watermarks and windows.

Preface

Who this book is for

What this book covers

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Free Chapter

Configuring Kafka

Kafka in a nutshell

Kafka installation

Running Kafka

Running Confluent Platform

Running Kafka brokers

Running Kafka topics

A command-line message producer

A command-line message consumer

Using kafkacat

Summary

Message Validation

Enterprise service bus in a nutshell

Event modeling

Setting up the project

Reading from Kafka

Writing to Kafka

Running the processing engine

Coding a validator in Java

Running the validation

Summary

Message Enrichment

Extracting the geographic location

Enriching the messages

Extracting the currency price

Enriching with currency price

Running the engine

Extracting the weather data

Summary

Serialization

Kioto, a Kafka IoT company

Project setup

The constants

HealthCheck message

Java PlainProducer

Running the PlainProducer

Java plain consumer

Java PlainProcessor

Running the PlainProcessor

Custom serializer

Java CustomProducer

Running the CustomProducer

Custom deserializer

Java custom consumer

Java custom processor

Running the custom processor

Summary

Schema Registry

Avro in a nutshell

Defining the schema

Starting the Schema Registry

Using the Schema Registry

Java AvroProducer

Running the AvroProducer

Java AvroConsumer

Java AvroProcessor

Running the AvroProcessor

Summary

Kafka Streams

Kafka Streams in a nutshell

Project setup

Java PlainStreamsProcessor

Running the PlainStreamsProcessor

Scaling out with Kafka Streams

Java CustomStreamsProcessor

Running the CustomStreamsProcessor

Java AvroStreamsProcessor

Running the AvroStreamsProcessor

Late event processing

Basic scenario

Late event generation

Running the EventProducer

Kafka Streams processor

Running the Streams processor

Stream processor analysis

Summary

KSQL

KSQL in a nutshell

Running KSQL

Using the KSQL CLI

Processing data with KSQL

Writing to a topic

Summary

Kafka Connect

Kafka Connect in a nutshell

Project setup

Spark Streaming processor

Reading Kafka from Spark

Data conversion

Data processing

Writing to Kafka from Spark

Running the SparkProcessor

Summary

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Message Enrichment

To fully understand this chapter, it is necessary to have read the previous chapter that focused on how to validate events. This chapter is focused on how to enrich events.

In this chapter, we will continue using the systems of Monedero, our fictitious company that is dedicated to the exchange of cryptocurrencies. If we remember in the previous chapter, the messages of Monedero were validated; in this chapter, we will continue with the same flow, but we will add one more step of enrichment.

In this context, we understand enrichment as adding extra data that was not in the original message. In this chapter, we will see how to enrich a message with geographic location using the MaxMind database and how to extract the current value of the exchange rate using the Open Exchange data. If we remember the events that we modeled for Monedero, each one included the IP...