Graph databases are becoming more important to analytics by offering a capability to store relationships and perform unique algorithms. Graph databases show relationships, true. But the real power might just be in the difficult analysis they can perform. In the graph database world, the graph relationship diagram highlights one of the unique values of graph, namely the ability to keep track of connections in the data. Graph visualizations are the first place to start when it comes to understanding the connections in the data and how the puzzle fits together. However, it is just one of the features that makes graph databases potentially valuable for your organization. Let’s look at a couple of examples of that potential and how they come together to empower analytics. Graph Algorithms Even though you may not necessarily visualize certain algorithms with traditional graph ball visualization, graph algorithms including Pagerank, shortest path, all paths and ...
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, J...