Neo4j

Native graph database platform built for storing, querying, and analyzing connected data. Treats relationships as first-class citizens instead of foreign keys, so traversing networks and pattern matching stays fast and intuitive without complex JOINs.

Trusted by enterprises such as Uber, Intuit, Merck, BNP Paribas, and Transport for London for fraud detection, knowledge graphs, and real-time analytics. Differentiates from relational databases by modeling data as nodes and edges, with Cypher providing a declarative query language for graph patterns, and from other graph options with mature tooling, 65+ built-in graph algorithms, and broad driver support.

Key capabilities:

• Native graph storage with nodes, relationships, and properties optimized for traversal
• Cypher query language for declarative pattern matching without nested JOINs
• Graph Data Science library with pathfinding, centrality, community detection, and embedding algorithms
• Import from CSV, JSON, APIs, and integrations with Kafka and Spark
• Official drivers for Java, Python, Node.js, Go, .NET, and REST API access
• Self-managed deployment on any cloud or on-premises, with managed cloud options

Teams use Neo4j for fraud and anomaly detection, recommendation engines, knowledge graphs for AI and RAG, supply chain and network analysis, and identity resolution. Developers model domain relationships directly, write Cypher queries for multi-hop traversals, and visualize graphs with built-in tools. Integration with data warehouses and analytics platforms supports hybrid workflows.