When modeling data in a graph, you'll constantly face a fundamental trade-off: data integrity versus query performance. A highly normalized model, where every piece of information exists in exactly one place, is clean and easy to maintain. However, it can lead to slower queries. On the other hand, a denormalized model, which strategically duplicates data, can make your most common queries run incredibly fast but adds complexity to your updates.

Unlike in the relational world where normalization is often the default, graph databases give you the flexibility to choose the best approach for your specific use case. The key is to model your data to answer your most important questions as efficiently as possible.

In a graph context, normalization means avoiding data redundancy. You break down information into distinct nodes and connect them with relationships. This ensures that if a piece of data changes, you only need to update it in one place.

Imagine a social media platform. A user lives in a city and makes several posts. A normalized approach would be to have a User node, several Post nodes, and a City node. The user is connected to their posts, and also to the city they live in. You wouldn't store the city name as a property on every single Post node created by that user.