Graph Databases Are Booming
What is a graph database? And why should we care?
Graph DBs are not new, but adoption is growing quickly because so much of our data involves connections and relationships.
Graph databases are organized around the concept of nodes—with each node representing an entity such as a person or product—and the relationships between and among nodes. Social networks are sometimes offered as examples of how graph databases connect the dots, but there are many other use cases: fraud detection, product recommendations, physical networks, and more.
Gartner forecasts rapid adoption of these purpose-built databases. Earlier this year, the advisory firm estimated that graph technologies were used in only 10% of data and analytics innovations, but predicted that will jump to 80% by 2025.
My recent article on Acceleration Economy provides updates on Memgraph, ArangoDB, and Nebula Graph, but the really big news in this space was Neo4j's $325 million funding round a few months ago.
Here’s the article: How Data Relationships Are Driving Graph Databases—and Big Investments
To really appreciate the potential for graph databases, look at the use cases. One of my favorites is Neo4j's demo of a graph database that comprised more than 200 billion nodes and 1 trillion relationships—the equivalent of a social graph detailing how every person on Earth is connected.