Graph Databases & AI: Why Graph Databases Beat SQL
Last Updated on December 29, 2025 by Editorial Team
Author(s): Alok Choudhary
Originally published on Towards AI.
Everything about graph databases: Stop struggling with JOIN queries. Learn Cypher queries, vs SQL comparison, and building RAG applications with LangChain in 2026.
In today’s data-driven world, the way we store and retrieve information has evolved significantly. While traditional databases have served us well for decades, there’s a growing need for systems that can handle complex relationships between data points more efficiently. This is where graph databases come into play, offering a revolutionary approach to data management that mirrors how we naturally think about connections in the real world.

This comprehensive guide will take you through everything you need to know about graph databases, from understanding knowledge graphs to comparing graph databases with traditional SQL databases, alongside practical applications such as Neo4j and Cypher queries, ultimately illustrating how these technologies integrate with modern AI and machine learning systems.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.