‘Graph data’ is normally used to describe data that is highly interconnected.
Transportation routes, social media friends or connections, organizational charts, genealogical charts,
product and customer data, electrical grids and pipelines…the scenarios where you can find them are endless.
The most common way graph data is modeled in the relational world is by using many-many-relationships..querying such structures can be very painful and expensive in terms of time and computing power.
Modeling it the graph way instead allows for efficient querying, detection of associations and patterns, performing affinity analysis, and adding business value in a variety of different ways.
There are dedicated graph databases that do this..such as Neo4j..
but what if most of your data is in a relational engine already?
What if you want to keep the gains of a relational engine such as security, ACID properties, and high availability
and still, do some graph modeling?
SQL Server 2016+ supports graph data modeling as well.
In this talk on SQL Graph Revealed we will learn about origins of graph theory, components of graph data, and advantages of modeling relationships using graph capabilities of SQL Server.