Graph Database
A graph database stores data as nodes (entities) and edges (relationships), optimized for queries over connected structures.
Graph databases store data as nodes and edges ā optimized for connected queries like social networks, recommendations, and fraud detection.
Explanation
Unlike relational DBs that require JOINs, graph databases traverse relationships directly. This makes them ideal for social networks, recommendation systems, and fraud detection.
Marketing Relevance
Graph databases enable Customer 360 views, influencer mapping, and real-time recommendations for marketing teams.
Common Pitfalls
Not every problem is a graph problem; lacking team expertise; scaling very large graphs without partitioning.
Origin & History
Neo4j (2007) was the first production-ready graph database. Amazon Neptune (2017) and Azure Cosmos DB (2017) brought managed graph services. In 2024, graph DBs process trillions of edges in real-time.
Comparisons & Differences
Graph Database vs. Relationale Datenbank
Relational DBs use tables with JOINs (O(nĀ²) for multi-hop); Graph DBs traverse relationships in O(1) per hop.
Graph Database vs. Vector Database
Vector DBs find similar embeddings (semantic similarity); Graph DBs find explicit relationships (structural connections).