This practical guide, subtitled "Real-Time Data and Stream Processing at Scale" shows how to use the Kafka open source streaming platform to handle real-time data feeds. Authors Neha Narkhede, Gwen Shapira and Todd Palino are engineers from Confluent and LinkedIn who are responsible for developing Kafka, and in this book they explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform.
The book has detailed examples to illustrate Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.
Author: Neha Narkhede, Gwen Shapira and Todd Palino
Date: Sept 2017
Audience: Developers using Kafka
Category: Data Science
- Understand publish-subscribe messaging and how it fits in the big data ecosystem.
- Explore Kafka producers and consumers for writing and reading messages
- Understand Kafka patterns and use-case requirements to ensure reliable data delivery
- Get best practices for building data pipelines and applications with Kafka
- Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks
- Learn the most critical metrics among Kafka’s operational measurements
- Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems
For recommendations of books about working with Big Data see Reading Your Way Into Big Data in our Programmer's Bookshelf section.
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