PuppyGraph Blog

Discover the versatility of graph databases with our guide on seven impactful use cases.

Learn what an on-path attack is, how attackers intercept network traffic, common attack types, risks, and methods to detect and prevent on-path attacks.

Learn what a code graph is, how it’s built, and how developers use it to analyze dependencies, visualize code structure, and improve software quality.

Discover the differences between AWS Neptune and DynamoDB, from graph vs NoSQL data models to scalability, performance, and real-world use cases. Find the right AWS database for you.

Learn what an agent harness is, how it works, key components, architecture, and why it’s critical for building reliable AI agents in production.

Explore the best ArangoDB alternatives for graph database solutions. Compare performance, scalability, and features of seven top tools to find your ideal match.

In this post, we dive into topics like whether engineers can use PostgreSQL as a graph database, how to do graph modeling in PostgreSQL, the challenges and best practices, and how PuppyGraph can help you query PostgreSQL data as a graph.

Delve into the intricate web of fraud with fraud graphs. Learn how fraud patterns are uncovered and explore advanced techniques for detection and prevention. Stay ahead in the battle against fraudsters.

Explore how Cyber Graphs enhance cybersecurity by detecting threats, mapping attack paths, and strengthening network security using advanced graph analytics.

Threat Graph helps cybersecurity teams to visualize, analyze, and track cyber threats in real-time. It helps organizations detect vulnerabilities, prevent attacks, and enhance security through advanced threat intelligence.

Learn how cyber threat analytics help detect, predict, and prevent threats, and enhance threat investigations with graph visualization and OTX integrations.

Learn how the Breadth-First Search algorithm works in graph traversal, including BFS steps, implementation, complexity analysis, and common real-world applications.

Explore GraphRAG architecture, including its core components, knowledge graph integration, retrieval pipeline, and LLM orchestration. Learn how GraphRAG improves contextual accuracy over traditional RAG systems.

Learn what strongly connected components are in graph theory, how SCC algorithms like Kosaraju’s and Tarjan’s work, and their practical applications in graph analysis.

Discover how graph databases help detect fraud in banking, fintech, and e-commerce by uncovering hidden relationships, spotting fraud rings, and reducing false positives with real-time analytics.