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The Internet of Things (IoT) has created new and challenging opportunities for Data Analytics. IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. Real-time Data Stream Processing is a natural answer for the majority of the goals of IoT platforms, but it has to deal with the highly variable and dynamic IoT environment. IoT applications usually consist of multiple technological layers connecting ‘things’ to a remote cloud core. These layers are generally grouped in two macro-levels: the edge-level (consisting of the devices at the boundary of the network near the devices that produce the data) and the core-level (consisting of the remote cloud components of the application). Real-time Data Stream Processing has to cope with a wide variety of technologies, devices and requirements that vary depending on the two IoT application levels. The aim of this work is to propose an adaptive microservices architecture for an IoT platform able to integrate real-time stream processing functionalities in a dynamic and flexible way, with the goal of covering the different real-time processing requirements that exist among the different levels of an IoT application. The proposal has been formulated for extending Senseioty, a proprietary IoT platform developed by FlairBit S.r.l., but it can easily be integrated in any other IoT platform. A preliminary prototype has been implemented as proof of concept of the feasibility and benefits of the proposed architecture.