Graphs are mathematical structures used to model many types of entities and relationships in physical, biological, social, and information systems. A graph consists of nodes or vertices (representing the entities in the system) which are connected by edges (representing relationships between those entities). Graphs, however, are more than just nodes and edges; they are powerful data structures you can use to represent complex dependencies in your data. These vertices and edges carry properties describing the characteristics of entities and relationships. Capturing all the entities of a complex system, users, relationships, and attributes into a graph model and then enriching the graph further, with relevant data from other sources, a data analyst could probe ownership of condominiums on a planetary basis or could look for biomarkers in blood samples.
Why should I be using graph?
How can graph help my buisness?
Who else is using graph?
Graph may sound hard and abstract, but they are not. They are now a necessary tool in a data scientist’s armory. Graph data structures are built from the same organization of rows and columns of data as relational databases, such as Oracle. Just as a mathematical graph with x and y axes imparts visual understanding, a data graph can give structure to connected data. In a social network, for example, revealing who posts with most impact, compared to who posts most, and who is most likely to be paying for their impact?
A graph approach allows a business to query data that is otherwise kept in separate datasets, perhaps by separate departments. Once ingested into a data graph, such data often provides insights which can help a company find value.
Netflix, Facebook, Google and just about every other Fortune 1000 company. There is an entire global community of graph users, often citizen data scientists, that has grown to be a large number over the last decade.