The emergence of Big Data some years ago denoted the challenge of dealing with huge collections of heterogeneous data continuously produced and to be exploited through data analytics processes. First approaches have addressed data volume and processing scalability challenges. Solutions can be described as balancing delivery of “physical” services of (i) hardware (computing, storage and memory), (ii) communication (bandwidth and reliability) and scheduling (iii) greedy analytics and mining processes with high in-memory and computing cycles requirements.

Due to the democratization Big Data management and processing is no longer only associated to scientific applications with prediction, analytics requirements, the homo technologicus requirements also call for Big Data aware applications related to the understanding and automatic control of complex systems, to the decision making in critical and non-critical situations. Big Data forces to view data mathematically (e.g., measures, values distribution) first and establish a context for it later.

This lecture will address different techniques for analysing and visualizing big data collections including a vision of the analytics process as a complex and greedy task and then visualization as out of the box solutions that can help to analyse and interpret big data collections.