Graph Databases Help Companies Unlock Connections Within Their Data

Thanks to Jim Webber, Chief Scientist at Neo4j, for sharing his thoughts on the current state of databases and their future with the influx of big data. Neo4j is the leading platform for graph technology and connected data, and it’s continuing to grow as it aims to corral unstructured data in companies across numerous industries.

Q: What are the keys to successfully implementing a graph database strategy?

A: Developers have to be willing to get out of the comfort zone they have with relational databases. Letting go of what you’re familiar with, even if the new method is far superior to your standard course of action, is a step that many people are reluctant to take. Graph databases provide degrees of freedom that can be scary at first, but in the end, are empowering as you begin to grasp the technology’s capabilities. The degrees of freedom available to you in graphs is initially bewildering — and I say that as a keen graph advocate — but once you’re over that curve, you’ll never go back to the implicit complexities of RDBMS or suffer the weak data models of NoSQL.

Q: How can companies benefit from graph databases?

A: First and foremost, there is an incredible leap in terms of performance when going from relational databases to graph databases. Our users regularly feedback that their queries go from minutes to milliseconds – orders of magnitude faster. Relational databases use set theory, so when dealing with huge data sets you still end up with lots of intermediate sets in your graph. Modern operating systems are wonderful and they won’t fail the database even when it consumes more memory than RAM, but it will spill over to virtual memory on disk. This means that your relational query will chug along at a disk speed.

The secret sauce with native graph databases is that we are doing pointer chasing — what we like to call constant time traversal. I think of it as sending a robot into a graph, where it then roams around in the corridors of the graph dataset to find what it’s looking for. This is very performant: on my bog-standard laptop, you can have fourteen to sixteen million traversals happening every second.

Perhaps just as importantly, graph databases are immensely helpful with modeling and communicating with business stakeholders. If you draw a data model on a whiteboard, that is the same model that is stored in the database. There is no need to normalize or denormalize the model, meaning that there is no technical obfuscation of the domain view. This allows people on the business side of things to look at this data in a language that is meaningful to them: their data is stored their way.

Q: How have databases changed most recently?

A: If you are looking back five years ago, the adoption of NoSQL was just becoming more accepted as an alternative to relational databases. Around two years ago though, it feels like graph databases began to really demonstrate distinction and unique business value. Nowadays, graphs are no longer such a niche market and are being adopted by many of the top companies across industries like retail, finance, healthcare, manufacturing, and security.