Weaknesses:
Scaling Challenges: Horizontal scaling can be more complex than other NoSQL types, especially for queries that require traversing the entire graph.
Not for Simple Data: Overkill for applications with data that has few relationships.
New Query Languages: Often use specialized graph query languages (e.g., Cypher for Neo4j, Gremlin for Apache TinkerPop), requiring a learning curve.
Ideal Use Cases:
Recommendation Engines: "Users who bought X also bought Y," product recommendations.
Fraud Detection: Identifying suspicious patterns and rings in transactions.
Knowledge Graphs: Representing complex domain knowledge and relationships.
Network and IT Operations: Mapping dependencies in IT infrastructure.
Supply Chain Management: Tracing parts and relationships in complex supply chains.
Prominent Examples: Neo4j, Amazon Neptune, ArangoDB, TigerGraph.
Beyond the Four Core: Other Specialized Database Paradigms
The "NoSQL" umbrella has expanded, and other purpose-built databases address even more specific needs:
5. Search Engines (Inverted Index): Full-Text Powerhouses
Concept: While not always classified purely as databases, these are dentist data critical components for applications needing fast, relevant full-text search. They build an "inverted index" mapping words to the documents in which they appear, enabling complex search queries, relevance scoring, and faceting.
Why Essential: Traditional databases are poor at full-text search.
Examples: Elasticsearch, Apache Solr, Apache Lucene (the underlying library).
6. Time-Series Databases (TSDB): For Event Streams