Building Event-Driven Microservices, 2/e

Event-driven microservices offer an optimal approach to harnessing event streams, reacting and responding to changes as they occur across your company. With this fully revised and updated guide, you’ll learn how to apply the principles of event-driven architecture to create event streams and build powerful microservice applications.

Author Adam Bellemare takes you through the process of creating event-driven microservice architectures, from first principles all the way to advanced applications. Covering events, event streams, and microservices, this book will give you powerful and reusable patterns for sharing and using important data all across your organization.

• The theory and principles of event-driven architectures
• How to design and build event-driven microservice architectures to deliver exceptional business value
• Event and event-stream design patterns, including schemas and evolution through time
• Microservice application patterns, both as singular services and as a collection of multiple services
• Tooling and techniques to get your event-driven microservice ecosystem off the ground and set yourself up for success
• Integrating event-driven applications into your existing architecture

About the Author

Adam Bellemare is a Staff Engineer, Data Platform at Flipp. He's held this position since 2017. He joined Flipp in 2014 as a senior developer at Flipp. Prior to that, he held positions in embedded software development and quality assurance. His expertise includes: Devops (Kafka, Spark, Mesos, Zookeeper Clusters. Programmatic Building, scaling, destroying); Technical Leadership (Bringing Avro formatting to our data end-to-end, championing Kafka as the event-driven microservice bus, prototyping JRuby, Scala and Java Kafka clients and focusing on removing technical impediments to allow for product delivery); Software Development (Building microservices in Java and Scala using Spark and Kafka libraries); and Data Engineering (Reshaping the way that behavioral data is collected from user devices and shared with our Machine Learning, Billing and Analytics teams).