Open Data is about supporting the insights in between what historically have been inaccessible data, protected by companies or other institutions. When data became more opened, it was possible to see a growing sector of activities exploring the opportunities it created. Business Intelligence, Analytics, and Machine Learning are some names very popular around the subject. This data-driven approach quickly became the key to success, and ideas were put to practice. Despite the popularity, the technical solutions of the software systems were yet naive.

When handling with large amount of data, specially open data, there’s an important lesson learned: systems got to be flexible, as data are. For that matter, Microservices have been widely adopted in systems that handle open data, due to its support for Extensibility. In this architecture, the system is maintained as a set of small, independent parts. Each part, called a microservice, has a specific responsibility, and the different microservices inter-exchange data to meet the system’s and user’s need. In this talk, we’re going through the most indispensable principles and practices when designing computing systems that are data-driven, perfectly tailored for Open Data. We’ll begin providing a perspective on how to establish the communication and shared data between microservices, and evolve into discussing ways to support seamless incorporation of new data-analysis services.