The article explains what graph computing is, why it outperforms traditional databases for relationship‑centric data, outlines its three core functions—graph query, graph computation, and graph learning—and provides a step‑by‑step guide for applying it to real‑world business scenarios.
This article explains how integrating graph‑based data models into cloud‑native observability platforms transforms isolated metric monitoring into a relational view, enabling powerful queries such as graph‑match and Cypher to perform fault impact analysis, root‑cause tracing, and security audits across services, pods, and infrastructure.
In modern cloud‑native systems, treating each service, container, or middleware as an isolated entity hides the essential connections between components, so this article explains how integrating graph‑based data models and query languages like graph‑match and Cypher unlocks powerful fault‑impact analysis, topology insights, and performance‑optimized troubleshooting.
Microsoft announced the open‑source Drasi system, a low‑code, graph‑query based platform that monitors logs, databases, and metrics to detect changes in real time, automatically triggering context‑aware actions without moving data to a central lake, aiming to simplify complex event‑driven architectures.
This article introduces graph databases as a NoSQL solution, explains property‑graph modeling, compares relational and document stores, evaluates several graph products, and details Dgraph’s architecture, indexing, query language, and real‑world business applications such as knowledge graphs and equity‑relationship analysis.
The forum explores the rapid growth and complexity of knowledge graphs, addressing storage and computation challenges through expert talks on graph database storage, query languages, practical implementation, and large‑scale financial knowledge graph platforms, offering attendees deep technical insights and hands‑on guidance.
