Data Stream Processing (DSP) is very crucial in many applications especially in today’s era of Big Data. DSP finds its importance in many areas concerning anomaly detection, stock market surveillance, network monitoring and many others, helping to obtain real-time insights to devise quick data-driven decisions. Eventually, DSP compromises of several tasks and the typical of them include frequency estimation, heavy hitter detection, heavy change detection, frequency distribution estimation and entropy estimation. Consequently, this presentation provides an overview of four scientific research papers in terms of their scientific problem studied, its importance and the formulated approach in each of the four area clusters namely Elasticity, Distributed processing, Sketches and Heavy hitters constituting for scalable stream processing and counting on conventional streams.