What is Graph Database
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.
What does it do/how does it work?
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.
Why is it important? Where is it used?
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.
FAQ
How are graph databases different from other NoSQL databases?
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.
When should you use a graph database?
Graph databases excel at managing highly connected data and are ideal for:
What are key challenges with graph databases?
However, graph databases also pose their own complexities around scale, algorithms, and querying.
What are some popular graph database tools and vendors?
References
Related Topics
Vector Database
A vector database is designed to efficiently store and query vector representations of data for applications like search, recommendations, and AI.
Graph Database
A graph database stores data in a graph structure with nodes, edges and properties to represent and query relationships between connected data entities.
Search Engine (Database)
A search engine database is designed to store, index, and query full text content to enable fast text search and retrieval.
Document Store
Document store database manages collections of JSON, XML, or other hierarchical document formats, providing querying and indexing on document contents.
RDF Store
An RDF store is a graph database optimized for storing and querying RDF triple data to represent facts and relationships.
Key-value Store
A key-value store is a type of NoSQL database optimized for storing, retrieving and managing associative arrays of key-value pairs.
Spatial Database
A spatial database is a database optimized to store, query and manipulate geographic information system (GIS) data like location coordinates, topology, and associated attributes.
Relational Database
A relational database is a type of database that stores and provides access to data according to relations between defined entities organized in tables.