TigerGraph

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Overview

TigerGraph is an enterprise-scale graph database designed for real-time AI and analytics. It analyzes massive, highly connected data, enabling organizations to understand relationships between people, transactions, devices, products, and events. TigerGraph's native massively parallel graph architecture supports queries across billions of vertices and edges with <100ms latency. It is built for applications like fraud detection, entity resolution, customer intelligence, and AI systems. TigerGraph provides GSQL, OpenCypher, and ISO GQL query languages and deploys anywhere via Kubernetes, offering auto-scaling and seamless integration with DevOps pipelines. It targets developers and data scientists looking to unlock hidden value in connected data.

Common tasks

Graph data modeling Real-time graph analytics Fraud detection Entity resolution Customer 360 analysis Supply chain optimization Cybersecurity threat detection

FAQ

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What is a graph database?

A graph database is a database that uses graph structures with nodes, edges, and properties to represent and store data.

What are the advantages of using a graph database?

Graph databases excel at managing relationships between data points, enabling faster and more insightful queries for connected data.

What is GSQL?

GSQL is TigerGraph's native query language designed for deep link analytics and graph traversal.

What is GraphRAG?

GraphRAG combines the power of graph databases with Retrieval Augmented Generation (RAG) to provide more accurate and explainable generative AI responses.

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