Organizations today often struggle when trying to balance business requirements with ever-increasing volumes of data. Yet the need to take advantage of real-time data continues to grow among the most competitive digital industries. Conventional system architectures may not be up to the task. With this practical guide, you’ll learn how to leverage large-scale data usage across the business units in your organization using the principles of event-driven microservices.

Author Adam Bellemare from Flipp takes you through the process of building a complete microservice-powered organization. You’ll reconsider how data is produced, accessed, and used across your organization. Discover powerful yet simple patterns for unlocking the value of this data. Incorporate event-driven design and architectural principles into your own systems. And completely rethink how your organization delivers value by unlocking real-time access to data at scale.

You’ll learn:

How to leverage event-driven architectures to deliver exceptional business value

The role of microservices in supporting event-driven design

Architectural patterns to ensure success both within and between teams in your organization

Application patterns for developing powerful event-driven microservices

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).