Kafka Up and Running for Network DevOps (Network DevOps Series: Kafka Up and Running)
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Kafka Up and Running for Network DevOps

Set Your Network Data in Motion

About the Book

Today's network is about agility, automation, and continuous improvement. In Kafka Up and Running for Network DevOps, we will be on a journey to learn and set up the hugely popular Apache Kafka data messaging system. Kafka is unique in its principle to treat network data as a continuous flow of information that can adapt to the ever-changing business requirements. Whether you need a system to aggregate log messages, collect metrics, or something else, Kafka can be the reliable, highly redundant system you want.

We will begin by learning about the core concepts of Kafka, followed by detailed steps of setting up a Kafka system in a lab environment. For the production environment, we will take advantage of the various public cloud provider offerings. Next, we will set up our Kafka cluster in Amazon Managed Kafka Service to host our Kafka cluster in the AWS cloud. We will also learn about AWS Kinesis, Azure Event Hub, and Google Cloud Put/Sub. Finally, the book will illustrate several use cases of how to integrate Kafka with our network from data enhancement, monitoring, to an event-driven architecture.

The Network DevOps Series is a series of books targeted for the next generation of Network Engineers who wants to take advantage of the powerful tools and projects in modern software development and the open-source communities.

About the Author

Eric Chou
Eric Chou

Eric Chou is a seasoned technologist with over 20 years of experience. He has worked on some of the largest networks in the industry while working at Amazon AWS, Microsoft Azure, and other Fortune 500 companies. Eric is passionate about network automation, Python, and helping companies build better security postures. Eric is the author of the top-selling title, 'Mastering Python Networking' (Packt Publishing). Eric is the primary inventor for two U.S. patents in IP Telephony. He shares his deep interest in technology through his books, classes, blog, and contribute to some of the popular Python open-source projects.

Eric Chou

Episode 222

Reader Testimonials

Emil Garipov
Emil Garipov

It's a wonderful book. Started reading it and already love everything about it. Everything is perfectly structured. The best thing for people who are not native English like me, is that the book is written simply, not overloaded with any unnecessary information. -- From Twitter (https://twitter.com/gissarsky/status/1463359334176411653?s=20)

Daniel Wade
Daniel Wade

The book is a great introduction to Kafka. It has plenty of practical examples on how to set it up in multiple environments, including on-prem and different public cloud providers. As a network engineer, I enjoyed the chapter on how to read from log files. I highly recommend this book if you want to start learning Kafka! -- From Amazon (https://www.amazon.com/gp/product/B09LRC5YZT/ref=dbs_a_def_rwt_bibl_vppi_i2)

Table of Contents

  • Introduction
    • What is Kafka
    • Why do we need Kafka
    • Prerequisites for this book
    • Who this book is for
    • What this book covers
    • Download the example code files
    • Conventions used
    • Get in touch
  • Chapter 1. Kafka Introduction
    • History of Kafka
    • Kafka Use Cases
    • Disadvantages of Kafka
    • Kafka Concepts
    • Conclusion
  • Chapter 2. Kafka Installation and Testing
    • Network Lab Setup
    • Kafka Installation Overview
    • Install Java
    • Download Kafka
    • Configure Zookeeper
    • Configure Kafka
    • Start Zookper and Kafka manually
    • Test the Kafka operations
    • Configure System Services
    • Conclusion
  • Chapter 3. Kafka Concepts and Examples
    • Producers: Writing Messages
    • Consumers: Receiving Messages
    • Offsets in Action
    • Kafka Topic Administration
    • Replication
    • Conclusion
  • Chapter 4. Hosted Kafka Services
    • AWS Managed Kafka Service
    • Amazon MSK Costs
    • Launch Amazon MSK Cluster
    • Client Setup
    • Produce and Consume Data
    • Conclusion
  • Chapter 5. Cloud Provider Messaging Services
    • Amazon Kinesis
    • Amazon Kinesis Example
    • Azure Event Hub
    • Azure Event Hub Example
    • Google Cloud Pub/Sub
    • GCP Pub/Sub Python Example
    • Conclusion
  • Chapter 6. Network Operations with Kafka
    • Install Docker
    • Install Elasticsearch
    • Install Kibana
    • Network Data Feed
    • Network Data Pipeline
    • Network Log as a Service
    • Conclusion
  • Chapter 7. Other Kafka Considerations and Looking Ahead
    • Hardware Considerations
    • Kafka Broker and Topic Configurations
    • Schema Registry
    • Kafka Stream Processing
    • Cross-Cluster Data Mirroring
    • Additional Resources
    • Conclusion
  • Appendix A. Installing Lab Instance in Public Cloud

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