A graph database is a database optimized for storing and querying highly connected data represented as nodes (vertices) and relationships (edges). Data is modeled as a network graph with entities as nodes linked by relationships enabling traversal queries.
Unlike relational databases, graph databases efficiently store complex hierarchical and networked data while still allowing schema flexibility. Popular graph database implementations include Neo4j, Amazon Neptune, and Microsoft Cosmos DB.
A graph database represents data as nodes connected by edges. Nodes store entities while edges represent relationships between entities. APIs allow storing nodes/edges and traversing graph structures using graph algorithms like shortest path finding, community detection etc.
Graph databases use index-free adjacency to efficiently traverse networks by directly following links between nodes. Their native graph storage and processing avoids complex expensive joins.
Graph databases excel at fast traversal operations across complex connections, which is very different from traditional databases. Use cases include social networking, knowledge graphs, fraud detection, master data management, network analytics, recommendation engines.
By directly modeling entity relationships as graphs, they underpin applications like social networks, knowledge management, drug discovery, network security that rely on analyzing connections and graph theory. Graph databases are often used together with vector databases and document stores for knowledge graph applications.
Unlike other NoSQL stores, graph databases natively represent data as nodes connected by edges instead of tables, documents or key-values. This allows efficiently traversing and analyzing relationships between entities.
Graph databases excel at managing highly connected data and are ideal for:
However, graph databases also pose their own complexities around scale, algorithms, and querying.
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