The analysis of graphs has become important in many domains such as social media, marketing, the life sciences, telecommunication, counter-terrorism and crime fighting. Here the graphs are often so big that specialized techniques and algorithms are necessary to compute the analysis, for example by distributing them in a Spark-cluster. Data analysis tasks on real-world web-scale datasets often involve analysing properties of the graphs represented by those datasets.
Graph databases can be seen as a special case of NoSQL databases, however they usually have very specific different implementation techniques and application domains that are different from other NoSQL databases. For example graph database play an important role in graph analytics for domains such as social media, the life sciences, telecommunication and crime fighting.
Many platforms for big data processing have emerged in recent years, and many are still emerging with different platforms focussing on different types of data processing such as memory-based data processing, graph processing or stream-based processing.