From a Monolithic Big Data System to a Microservices Event-Driven Architecture

[Context] Data-intensive systems, a.k.a. big data systems (BDS), are software systems that handle a large volume of data in the presence of performance quality attributes, such as scalability and availability. Before the advent of big data management systems (e.g. Cassandra) and frameworks (e.g. Spark), organizations had to cope with large data volumes with custom-tailored solutions. In particular, a decade ago, Tecgraf/PUC-Rio developed a system to monitor truck fleet in real-time and proactively detect events from the positioning data received. Over the years, the system evolved into a complex and large obsolescent code base involving a hard maintenance process. [Goal] We report our experience on replacing a legacy BDS with a microservice-based event-driven system. [Method] We applied action research, investigating the reasons that motivate the adoption of a microservice-based event-driven architecture, intervening to define the new architecture, and documenting the challenges and lessons learned. [Results] We perceived that the resulting architecture enabled easier maintenance and fault-isolation. However, the myriad of technologies and the complex data flow were perceived as drawbacks. Based on the challenges faced, we highlight opportunities to improve the design of big data reactive systems. [Conclusions] We believe that our experience provides helpful takeaways for practitioners modernizing systems with data-intensive requirements.