Graph database
A graph database is a database system that represents data as entities and their relationships, typically using nodes and edges, and supports queries over that structure.
Graph Algorithms for Data Science
A graph database is a database designed to store and query entities and their relationships directly.
- How it is used
- Supports exploration of highly connected, variable data through relationship-centered queries.
- How deep it goes
- Introduces graph storage and modeling, then defers graph algorithms and machine learning to later milestones.
- Examples
- Uses NASA Lessons Learned records, a Twitter social network, and extracted Jon–WORKS_AT–W3C and Jon–RESIDES–Bahamas triples.
- What you can do after
- Evaluate/understand when relationship-heavy data benefits from graph modeling and connected queries.
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AI Agents and Applications
A graph database stores entities and relationships as connected data that can be queried by those relationships.
- How it is used
- Supports knowledge-intensive retrieval, agent memory, relationship tracking, planning, inference, and explainable answers.
- How deep it goes
- Defines nodes, edges, triples, and query languages, then sketches LLM-assisted extraction and generation without detailing database operations.
- Examples
- Uses Roberto, InterMilan, and SerieA triples plus a Knowledge Graph RAG pipeline and schema-constrained Cypher prompt.
- What you can do after
- Evaluate graph-backed RAG designs and understand how LLMs extract relationships, generate queries, and synthesize graph results.
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Natural Language Processing in Action, Second Edition
A graph database is a database system that stores entities and their relationships as graph structures for graph-oriented querying and analysis.
- How it is used
- Retrieves and explores connected facts in knowledge graphs through graph-oriented query languages such as SPARQL.
- How deep it goes
- Explains entity identifiers, property identifiers, and connected Wikidata queries, while deferring broader database trade-offs to exercises.
- Examples
- Uses Wikidata queries about Timnit Gebru, a scholarly article, its authors, and NetworkX-to-pandas graph traversal exercises.
- What you can do after
- Operate Wikidata’s REST API and SPARQLWrapper to retrieve entity identifiers, scholarly works, and connected authors.
Read how Natural Language Processing in Action, Second Edition explains graph database →
Data Analysis with LLMs
A graph data management system is software that stores and processes graph data consisting of entities and relationships.
- How it is used
- Supports natural-language question answering by processing graph-specific queries generated from questions.
- How deep it goes
- Defines graph database systems and their role, but does not detail query-language syntax or implementation.
- Examples
- Uses subway stations and connections to illustrate nodes, edges, labels, and properties.
- What you can do after
- Apply it by translating natural-language questions into Cypher, executing queries on a copied database, and checking their accuracy.
Related concepts
- Cypherused by · Cypher is a declarative graph query language for expressing patterns and retrieving, creating, or modifying property-graph data.
- databasekind of · A database is an organized collection of persistent data managed by software for storage, retrieval, querying, and modification.
- NoSQL databasekind of · A NoSQL database is a database system organized primarily around nonrelational data models or access patterns rather than relational tables.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.