Building Event-driven Microservices : Leveraging Organizational Data at Scale

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

Adam Bellemare is a principal technologist of the Technology Strategy Group at Confluent. Previously, he has worked as a staff engineer on data streaming platforms at Shopify and Flipp from 2014 to 2021. He started his work in event-driven systems at Blackberry in 2010, using custom-built streaming data pipelines for analyzing and reporting on cell-phone failures.

His expertise includes DevOps (Kafka, Spark, Mesos, Kubernetes, Solr, Elasticsearch, HBase, and Zookeeper clusters, programmatic building, scaling, monitoring); technical leadership (helping businesses organize their data communication layer, integrate existing systems, develop new systems, and focus on delivering products); software development (building event-driven microservices in Java and Scala using Kafka Streams, Flink, and Spark); and data engineering (reshaping the way that behavioral data is collected from user devices and shared within the organization).