Neo4j

Why Neo4j

Relational databases store data in tables. Graph databases store data as nodes and relationships. That distinction matters when your queries care more about connections than records.

Consider a social network. Finding a user is easy in SQL. Finding mutual friends is a join. Finding friends-of-friends-of-friends is a nightmare. The query explodes exponentially. In Neo4j, it’s a simple traversal. The database is designed for this.

The same pattern appears everywhere. Supply chains where components flow through networks of suppliers. Fraud detection where suspicious patterns emerge from transaction connections. Recommendations where user behavior creates implicit relationships between products. These problems have a graph structure. Fighting that structure with relational databases wastes engineering time and server resources.

Neo4j isn’t a replacement for PostgreSQL. It’s a specialized tool for relationship-heavy problems. We know when that specialization pays off.

What We Build With It

Recommendation engines are the classic use case. We’ve built systems that understand not just what users bought, but how their behavior connects to other users and products. Collaborative filtering at scale. Content-based recommendations with semantic relationships. Hybrid approaches that combine both.

Fraud detection benefits from graph analysis. Fraudsters create networks of fake accounts, transactions, and identities. Detecting them means finding patterns: circular payments, clustered registrations, abnormal connection structures. SQL queries for this are complex and slow. Cypher queries express the patterns naturally.

Knowledge graphs power intelligent applications. We’ve built systems that connect entities, concepts, and facts into queryable structures. Question answering over company data. Semantic search that understands relationships between terms. Ontology-driven applications where the schema itself is meaningful.

Access control systems get complicated fast. Role hierarchies, group memberships, delegated permissions, resource ownership. Graph databases model this cleanly. Checking if a user can access a resource becomes a traversal query rather than a chain of joins.

Supply chain visibility requires tracing connections. Which suppliers feed into which components. Where bottlenecks exist. How disruptions propagate. Graph queries answer these questions directly.

Our Experience Level

We’ve designed and operated graph databases for production workloads. Not just toy examples. Real systems with millions of nodes and performance requirements.

Cypher is expressive but has pitfalls. We write queries that use indexes effectively, avoid Cartesian products, and handle variable-length paths without exploding memory. We know how Neo4j’s query planner works and how to guide it toward efficient execution.

Schema design in graphs differs from relational thinking. What becomes a node versus a relationship. When to add properties versus create separate nodes. How to model time and versioning in graph structures. We’ve navigated these decisions across multiple projects.

We understand Neo4j’s consistency model. Single-node deployments versus causal clusters. Read replicas and their latency characteristics. Transaction isolation and what it means for concurrent writes.

Integration is where graph databases get practical. Neo4j rarely exists alone. We’ve built ETL pipelines that sync data from relational databases. We’ve implemented APIs that combine graph queries with other data sources. We design architectures where Neo4j handles the relationship-heavy parts while other databases handle what they’re good at.

When to Use It (And When Not To)

Neo4j shines when:

• Queries traverse multiple relationships — More than two hops deep
• Relationship properties matter — When, how, and why things connect
• Pattern matching is central — Finding structures in connected data
• Real-time traversals are required — Sub-second queries across connections

Avoid Neo4j when:

• Data is mostly tabular — Rows with columns, occasional joins
• Aggregate queries dominate — Counts, sums, averages over large datasets
• Write throughput is extreme — Neo4j prioritizes read performance
• Your team doesn’t know graphs — The learning curve is real

The honest signal: if you’re writing recursive CTEs or self-joins in SQL to traverse relationships, graph databases are worth evaluating. If your queries are mostly filters and aggregations, stay with relational databases.

Common Challenges and How We Solve Them

Super nodes that slow everything down. Some nodes have millions of relationships. Traversing them is expensive. We design schemas to avoid super nodes, use relationship types to limit traversals, and sometimes partition data across multiple node types.

Memory consumption on large traversals. Variable-length path queries can consume memory exponentially. We use depth limits, filter early in traversals, and sometimes break queries into multiple steps with application logic.

Syncing data from relational sources. Most organizations have their core data in PostgreSQL or MySQL. We build reliable sync pipelines using change data capture, handle schema evolution, and manage consistency between systems.

Query performance regression. As data grows, some queries slow down unexpectedly. We monitor query performance, analyze execution plans with PROFILE and EXPLAIN, and refactor queries or schemas as patterns emerge.

Modeling time and history. Graphs are naturally snapshot-based. Modeling how relationships change over time requires careful schema design. We’ve built temporal graph models that track history without sacrificing query performance.

Graph databases require expertise. We have it.