Devices and sensors generate streams of data across a diversity of locations and protocols. That data usually reaches a central platform that is used to store and process the streams. Processing can be done in real time, with transformations and enrichment happening on-the-fly, but it can also happen after data is stored and organized in repositories. In the former case, stream processing technologies are required to operate on the data; in the latter batch analytics and queries are of common use. This research line explores runtimes to dynamically construct data stream processing topologies based on user-supplied code. These dynamic topologies are built on-the-fly using a data subscription model defined by the applications that consume data. Each userd efined processing consumes input data streams and may produce output streams that others can consume. A subscription-based programing model enables multiple users to deploy their own dataprocessing services. The runtime does the dynamic forwarding of data and execution of user code on the fly. A public prototype  runtime that leverages Apache STORM for parallel data processing has been developed, which combined with dynamic user-code injection provides multi-tenant stream processing topologies.