For most of my development career, I lived comfortably in the world of SQL. MySQL, PostgreSQL, even Oracle at one point — these tools were second nature. I could normalize tables in my sleep, write multi-join queries with confidence, and explain foreign keys to a junior dev in two minutes flat.
So when I first heard people talk about graph databases like Neo4j, my initial reaction was:
“Sounds interesting… but do I really need this?”
Turns out, yes — for the right kind of data and problems, I really did.
🤯 The Problem That Made Me Look Beyond SQL
It started with a project that involved modeling relationships between users, roles, permissions, and nested categories.
On paper, it looked simple:
Users have roles
Roles have permissions
Categories can have subcategories (multi-level)
Products belong to multiple categories
Admins can manage users in their region
…you get the idea.
But in SQL, representing this meant:
Lots of JOIN tables
Recursion using CTEs or self-joins
Increasingly complex, unreadable queries
Performance issues when nesting went beyond 2-3 levels
At some point, I found myself writing a query with 5+ joins, including a recursive category tree and user-region permission check. It worked — but barely. It was fragile and painful to maintain.
🔍 What Graph Databases Offered Instead
That’s when I started exploring Neo4j, just out of curiosity.
The first thing that hit me was how natural the data model felt.
Instead of:
Tables with IDs
Join tables
Foreign keys
I had:
Nodes (User, Role, Category)
Relationships (HAS_ROLE, BELONGS_TO, SUB_CATEGORY_OF)
Suddenly, the idea of querying “users who manage products in a subcategory of Electronics” felt like drawing a path — not performing surgery with JOINs.
🧠 Writing Queries with Cypher: A Mindshift
Here’s what I mean.
In SQL:
SELECT p.name
FROM products p
JOIN product_categories pc ON pc.product_id = p.id
JOIN categories c ON c.id = pc.category_id
WHERE c.parent_id = (
SELECT id FROM categories WHERE name = 'Electronics'
);
In Cypher (Neo4j):
MATCH (e:Category {name: 'Electronics'})<-[:SUB_CATEGORY_OF*]-(c:Category)<-[:BELONGS_TO]-(p:Product)
RETURN p.name
Boom. One line. And it reads like English:
Find all products that belong to a subcategory of Electronics.
That was the moment it clicked.
🧩 SQL Still Has Its Place (And I Still Use It)
Let me be clear: I didn’t abandon SQL. I still use it for:
Flat, tabular data
Transaction-heavy apps (orders, invoices, etc.)
Systems where relationships are shallow and predictable
But when I’m working with:
Hierarchical data
Access control systems
Social networks
Recommendation engines
Deeply connected entities
Graph databases make life so much easier.
📘 I Wrote a Book About This Journey
I realized a lot of developers like me might be curious about Neo4j and graph databases but feel unsure how to start — or skeptical that it's worth it.
So I wrote an ebook:
Relational to Graph: Rethink Data with Graph Databases
It’s not a dry manual — it’s my real-world journey from SQL to graph:
With side-by-side SQL and Cypher examples
Practical use cases
When to use graph vs relational
And how to think in relationships, not tables
🎯 Final Thoughts
Graph databases aren’t a replacement for relational databases. They’re a complement — a new tool in your dev toolkit for modeling real-world relationships in a cleaner, faster, and more intuitive way.
If you’ve ever struggled with complex joins, recursive CTEs, or deeply nested relationships, it might be time to rethink how you model your data.
Explore the graph.
👉 Grab the book on Amazon
https://www.amazon.com/dp/B0FBSZQ8M8
I wrote it for developers like you.
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