0 views collected around this technical thread.
This article presents a comprehensive overview of TuGraph-DB, covering the historical development of graph query languages, the capabilities and future plans of the TuGraph 4.0 query engine, and the proposed architectural evolution to support multiple languages and storage back‑ends.
This article introduces graph databases and explains why they are suited for relationship‑centric data, then details TuGraph’s key characteristics, architecture layers, supported query languages, analytics capabilities, market trends, roadmap plans, target users, and community resources, including a free cloud trial.
This presentation explains where knowledge resides in data architectures, demonstrates knowledge‑graph‑driven skill discovery, metadata management, and semantic search, and concludes with a comparison of GraphQL and Cypher for graph queries, illustrated with real‑world Neo4j case studies.
This article introduces graph databases, explains their underlying graph model, compares them with NoSQL and relational databases, reviews popular implementations such as Neo4j and JanusGraph, and demonstrates querying with the Cypher language, highlighting their advantages for complex relationship queries in modern data‑intensive applications.