Streaming processing has given rise to new computation paradigms to provide effective and efficient data stream processing. The most important features of these new paradigms are the exploitation of parallelism, the capacity to adapt execution schedulers, reconfigure computational structures, adjust the use of resources according to the characteristics of the input stream and produce incremental results. The Dynamic Pipeline Paradigm (DPP) is a naturally functional approach to deal with stream processing. This fact encourages us to use a purely functional programming language for DPP. In this work, we tackle the problem of assessing the suitability of using (parallel) Haskell to implement a Dynamic Pipeline Framework (DPF). The justification of this choice is twofold. From a formal point of view, Haskell has solid theoretical foundations, providing the possibility of manipulating computations as primary entities. From a practical perspective, it has a robust set of tools for writing multithreading and parallel computations with optimal performance. As proof of concept, we present an implementation of a dynamic pipeline to compute the weakly connected components of a graph (WCC) in Haskell (a.k.a. DPWCC). The DPWCC behavior is empirically evaluated and compared with a solution provided by a Haskell library. The evaluation is assessed in three networks of different sizes and topology. Performance is measured in terms of the time of the first result, continuous generation of results, total time, and consumed memory. The results suggest that DPWCC, even naive, is competitive with the baseline solution available in a Haskell library. DPWCC exhibits a higher continuous behavior and can produce the first result faster than the baseline. Albeit initial, these results put in perspective the suitability of Haskell’s abstractions for the implementation of DPF. Built on them, we will develop a general and parametric DPF in the future.