Massive amount of data sets are continuously generated from a wide variety of digital services and infrastructures. Examples of those are machine/system logs, retail transaction logs, traffic tracing data and diverse social data coming from different social networks and mobile interactions. Currently, the New York stock exchange produces 1 TB data per day, Google processes 700 PB of data per month and Facebook hosts 10 billion photos taking 1 PB of storage just to mention some cases. Turning these streaming data flow into actionable real-time insights is not a trivial task. The usage of data in real-time can change different aspects of the business logic of any corporation including real time decision making, resource optimization, and so on. In this paper, we present an analysis of different aspects related to real-time data analytics from an algorithmic perspective. Thus, one of the goals of this paper is to identify some new problems in this domain and to gain new insights in order to share the outcomes of our efforts and these challenges with the research community working on real-time data analytics algorithms.