Introduction to graph data modeling
Graph data modeling is the process of structuring data for a graph database, where entities (nodes) and their connections (relationships) are at the core. Unlike relational databases, which store data in rigid tables with predefined schemas, graph databases natively model real-world relationships, making it easier to explore and analyze connected data.
Instead of forcing data into rows and columns, graph databases naturally connect information, enabling intuitive and performant queries—especially in use cases where relationships matter, such as recommendation engines, fraud detection, knowledge graphs, and identity resolution.
Memgraph follows the Labeled Property Graph (LPG) model, where both nodes and relationships can have properties—allowing for a flexible and dynamic schema that evolves with your data. By structuring your graph efficiently, you can optimize for speed, reduce memory usage, and avoid unnecessary complexity in your queries.
Why is data modeling important in Memgraph?
- Well-structured data leads to more efficient graph traversals.
- A clean graph model prevents unnecessary data duplication, improving storage efficiency.
- As your graph grows, an optimized schema ensures smoother scaling without slowdowns.
- Unlike relational databases, graph models are dynamic—you can modify nodes and relationships without breaking existing structures.
What you’ll find here
This documentation will guide you through the best practices for structuring data in Memgraph.
- Graph data model: These pages explain Memgraph’s Labeled Property Graph (LPG) model, its core components, and how it compares to Resource Description Framework (RDF) for handling connected data.
- Modeling guides:
- Modeling a knowledge graph: Learn how to represent domain-specific knowledge in Memgraph by encoding entities, relationships, and semantics into a graph. Use real-world examples, like project management, to visualize how knowledge graphs can simplify complex queries and power AI-driven systems.
- Modeling a graph from a CSV file: This guide takes you step-by-step through the process of importing data from a CSV file into Memgraph and structuring it into an efficient graph model. It’s perfect for those working with raw tabular data and looking to translate it into a connected, queryable format.
- Best practices: It provides practical guidance for designing efficient and maintainable graph data models in Memgraph. It outlines common pitfalls, such as overcomplicating models, duplicating data, and neglecting indexing, and explains how to avoid them.
Need help with data modeling?
Schedule a 30 min session with one of our engineers to discuss how Memgraph fits with your architecture. Our engineers are highly experienced in helping companies of all sizes to integrate and get the most out of Memgraph in their projects. Talk to us about data modeling, optimizing queries, defining infrastructure requirements or migrating from your existing graph database. No nonsense or sales pitch, just tech.
