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A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization …