  Today, we have to deal with many data (Big data) and we need to make
decisions by choosing an architectural framework to analyze these data coming
from different area. Due to this, it become problematic when we want to process
these data, and even more, when it is continuous data. When you want to process
some data, you have to first receive it, store it, and then query it. This is
what we call Batch Processing. It works well when you process big amount of
data, but it finds its limits when you want to get fast (or real-time)
processing results, such as financial trades, sensors, user session activity,
etc. The solution to this problem is stream processing. Stream processing
approach consists of data arriving record by record and rather than storing it,
the processing should be done directly. Therefore, direct results are needed
with a latency that may vary in real-time.
  In this paper, we propose an assessment quality model to evaluate and choose
stream processing frameworks. We describe briefly different architectural
frameworks such as Kafka, Spark Streaming and Flink that address the stream
processing. Using our quality model, we present a decision tree to support
engineers to choose a framework following the quality aspects. Finally, we
evaluate our model doing a case study to Twitter and Netflix streaming.
