You're trying to answer 'who are all the people connected to this customer through shared projects, vendors, or introductions?'
Your relational database can do it, but it takes 12 JOIN statements and runs for 45 seconds.
The answer comes back as a spreadsheet. You still have to draw the connections yourself.
Some questions are about connections, not tables. Those questions need a different kind of storage.
ESSENTIAL for relationship-heavy data - fraud detection, social networks, recommendation engines.
A graph database stores data as nodes (things) and edges (connections between things). Instead of 'Customer ID 47 has Order ID 123,' you have 'Customer [PLACED] Order.' The relationship itself becomes a first-class citizen with its own properties.
The power isn't in storing the data - it's in traversing it. 'Find all customers who bought products also bought by customers who attended the same event as me' is one query, not twelve JOINs. And it runs in milliseconds because the relationships are pre-computed, not calculated at query time.
Graph databases don't replace relational databases. They solve a different problem: when the connections ARE the data.
Graph storage solves a universal problem: how do you efficiently traverse multi-hop relationships without exploding query complexity?
Store relationships as first-class objects, not foreign keys. Pre-compute connection paths so traversal is O(1) per hop instead of O(n) table scans. Query by pattern matching, not by JOIN conditions.
Select a target person and maximum hops. Watch the graph database find all paths and rank them by connection strength.
You → Mike Chen: Worked together at Stripe 2019-2021
Mike Chen → Lisa Wang: Former colleagues at Oracle
Lisa Wang → Sarah Lee: Co-founded a project together
You → David Park: Met at SaaStr 2023
David Park → Emma Davis: Emma invested in StartupX
Emma Davis → Sarah Lee: VentureY invested in Acme
You → Mike Chen: Worked together at Stripe 2019-2021
Mike Chen → Sarah Lee: Connected on LinkedIn
You → David Park: Met at SaaStr 2023
David Park → James Smith: Both spoke at TechSummit
James Smith → Sarah Lee: Same company (Acme Inc)
The things in your graph
Nodes are your entities: customers, products, events, documents. Labels categorize them: a node can be both a 'Person' and an 'Employee.' Properties store attributes: name, email, created_at. Think of nodes as rows in a table, but without the rigid schema.
The connections between things
Edges connect nodes with typed, directed relationships: Customer -[PURCHASED]-> Product. Edges can have properties too: purchase date, quantity, discount applied. The direction matters: 'manages' is different from 'managed by.'
Finding paths through the graph
Queries describe patterns: 'Find all paths from Person A to Person B through shared Projects.' The database walks the graph, following edges, matching patterns. Each hop is constant time because relationships are indexed at write time.
Your sales team needs warm introductions. In 50ms, the graph returns: 3 paths through shared LinkedIn connections, 2 through conference attendees, 1 through a mutual investor. With relationship strength scores.
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You put your invoice line items in a graph because 'everything is connected.' Now simple aggregations like 'total revenue by month' require traversing millions of edges. Your accountant is furious.
Instead: Use graphs for relationship-heavy queries. Keep tabular data in relational databases. Often you need both.
You create a 'Company' node that connects to all 50,000 employees, all 10,000 products, and all 2 million orders. Now every query that touches the company node scans millions of edges.
Instead: Break super nodes into intermediate nodes. Use 'Department' nodes between Company and Employee. Partition by time or category.
You model 'Person KNOWS Person' as bidirectional by creating two edges. Now you have data duplication, and 'friends of friends' returns duplicates. Or worse, you model 'REPORTS_TO' as undirected and can't tell who manages whom.
Instead: Model direction intentionally. Use bidirectional traversal in queries when needed, but store edges with clear direction.
You've learned how to store and query data as nodes and relationships. The natural next step is understanding how to build knowledge graphs that AI systems can traverse for context.