Honestly, when I first heard about Neo4j, I kind of brushed it off. “Graph database?” sounded like some fancy buzzword to me—just another tool hyped up as the next big thing. I didn’t really buy it.
Fast forward a few weeks, and I was drowning in this project at work. The task sounded simple on paper: map out people, companies, and financial transactions. My boss explained it like, “Just throw it into a database and run some queries.” Cool, easy enough… except it wasn’t.
The deeper I got into the data, the messier it looked. Everything was connected in ways I hadn’t thought about. One person sitting on multiple boards, companies sharing investors, investors linked to other deals—it turned into a spider web. And I was supposed to trace how money moved through all of it.
My SQL queries got ridiculous. I’m talking 15+ JOINs, huge nested queries, and even then, someone would ask, “Can you show me everyone tied to XYZ Corp, but only if they’re within 3 degrees of separation?” and it was back to square one. I spent too many late nights banging my head on this.
Then a teammate (Marcus—appreciate you, man) mentioned he’d used Neo4j for something similar. At that point I figured, why not? I was already behind anyway.
That was the turning point. Instead of forcing the data into tables, I modeled it the way it actually worked—a network. People, companies, transactions, all as nodes, with their relationships linking them together.
And suddenly, the query that used to take me hours in SQL—“show connections within 3 hops”—was literally a few lines in Cypher. I could actually read the query and understand it.
Neo4j isn’t perfect. It has quirks, and I had to climb a bit of a learning curve. But for that project? Total lifesaver.