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What are graph databases?


The description of graph databases that you get when you google it are mostly academic. I see a lot of descriptions about graph databases that talk about seven bridges in Königsberg or Berners-Lee, the inventor of the internet. There are theories and visions which are fine, but for me, I still think it’s important to lead with the relevance. Why are graph databases important to you?

Imagine the data that’s stored in a local restaurant chain. If you were keeping track, you’d store customer information in one database table, the items you offer in another and the sales that you’ve made in a third table. This is fine when I want to understand what I sold, order inventory and who my best customer is. But what’s missing is the connective tissue, the connection between the items, along with function in the database that can let me make the most of it.
A graph database stores the same sort of data, but is also able to store linkages between the things.  John buys a lot of Pepsi, Jack is married to Valerie and buys different drinks. I don’t have to perfom JOINs to understand how I should market to each individual customer.  I can see the relationships in the data without having to make a hypothesis and test it.

Examples of applications for the graph databases
Semantic Info Stored
Example
Use Case
Ownership
Susan owns a Honda
Buyer Intent
Interest
Steve is interested in Football
Designed by
Frank Lloyd Wright designed the Guggenheim
Knowledge Graph
<classification>
Guggenheim is a museum
Connections
via port e.g. server1 connected via port 8080 to server2
Network/IT operations
Is associated with 

e.g gene is associated with cancer
Life Sciences
Many more
This new connected information layer does a lot for you. It’s not just about buyer intent, but it could be helpful in a lot of use cases (see table 1).
Since databases are designed with tables, not the linked data, SQL won’t do anymore. This has given rise to SQL-like languages (but different) like SparQL, Gremlin and Cypher to name a few.  A major difference is the analytical functions you need to act upon the linked data. If I wanted to find the most popular time to buy a certain product on your web site, or if I wanted to rank popularity of an item, for example, there’s new syntax for that. You need to learn the language of connected data to make the most of it.

Can’t you can do that with a RDBMS?

Yes, it is possible to create these linkages in a traditional RDBMS. However, to perform these tasks in traditional databases, database administrators have toiled to maintain unique keys and reconstruct relationships with JOINs. If graph databases are used, both the subject and its relationship, known as subjects and predicates, are already known.  There’s no need to reconstruct the connections.
Inferring that Zoe is the daughter of Mary if you have previously defined that Mary is the mother of Zoe is another example. You do not necessarily need to specifically define both relationships because graph databases are pretty smart about this. By comparison, relational database cannot understand anything it isn’t defined. Therefore, this inferencing capability has clear value when looking at interests, households and communities.

If you want to learn more about SparQL, check out one of the many SparQL tutorials online.  There’s ample opportunity to try a graph database, like AnzoGraph.



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