Data Structures such as graphs provide all kinds of information about relationships in data, whether they're hierarchical or networked. These Data Structures consist of Nodes and Relationships between those Nodes. Properties such as Nodes and Relationships can also be classified under Labels and Relationship Types. Relational Databases have evolved to meet the needs for Graph Data Structures due to the increasing need for quick response time when retrieving these types of Data Structures, but also because this type of Database allows one to organize their data with a more significant amount of flexibility than traditional Databases which typically require every Node (or even Relationships) to fit into the same Categories or Attributes.
Answer business-critical questions and make predictions
Instead of looking at rows or columns, graphs depict data from an entirely different perspective. With an eye for how the data relate to one another - what this means in relation to each other - it's easier to see patterns or determine new insights that may not have been previously available.
No more complex joins, that don't scale
Cypher is a very simple, intuitive, and user-friendly query language that has the power to understand how data connects together. It’s written in such a way that it creates graphs where one could see all the relationships and clusters between points. Even when you're looking for patterns or insights within data, Cypher queries are much simpler and easier to write than joining two huge tables using SQL syntax.
Relational vs Graph
Eliminating joins, finding hidden connections – these are just a couple of reasons why using Graph makes managing data much easier.
Eliminate Joins
Data is stored as it's connected. The graph does not have tables, so there's nothing to join. This expense and time sink disappears in a native graph database.
Find Patterns, Paths, and Hidden Connections
Finding paths is torture in SQL because you're asking a graph question about a table. The graph makes native relationships help you see how novel ideas, people, and products are connected. Generate recommendations, root out fraud, and see the real pattern behind the matrix.
Get a Handle on Many-to-Many Relationships
With traditional tables and BI, sometimes you can't see the forest (what your data is saying) for all of the trees (the individual data items). Graphs are flexible, letting you catalog just about anything and keep it connected without losing the big picture view.
Navigate Complex Hierarchies
Graphs make it a snap to handle multi-level data. Extract, aggregate, summarize and traverse hierarchies no matter the complexity – all at speed.
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