# 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](https://memgraph.com/docs/data-modeling/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)](https://memgraph.com/docs/data-modeling/graph-data-model/lpg-vs-rdf) for handling connected data.
- [Modeling guides](https://memgraph.com/docs/data-modeling/modeling-guides):
    - [Modeling a knowledge graph](https://memgraph.com/docs/data-modeling/modeling-guides/model-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](https://memgraph.com/docs/data-modeling/modeling-guides/model-a-graph-from-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](https://memgraph.com/docs/data-modeling/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.

  
> **Info**
>
> If you have a SQL data model and
>   want to migrate to Memgraph, you can try out our [Agent](https://memgraph.com/docs/ai-ecosystem/agents)
>   that leverages the LLM to automate the process of modeling and migration.

## 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.

![](https://memgraph.com/docs/pages/getting-started/memgraph-office-hours.svg)

- [Book a call](https://memgraph.com/office-hours)