How Two Forbes Top 20 Businesses Transformed Their Core Rules Engines
In today's rapidly evolving business landscape, agility and efficiency are crucial for staying competitive. To achieve this, businesses rely on rules engines to automate decision-making processes efficiently and consistently. However,…
Supercharging Fraud Detection: How a Leading Financial Institution Utilizes TigerGraph for Real-Time Entity Resolution
In today's fast-paced financial landscape, real-time fraud detection has become essential for safeguarding customer assets and preserving trust in financial institutions. One leading investment bank recently embraced an innovative solution…
Graphs for Good: Transforming Refugee Support with UAWelcome’s Revolutionary Project
In a world where technology continually evolves, one innovative project stands out as a beacon of hope—UAWelcome. Harnessing the power of graphs for good, UAWelcome is revolutionizing the way refugees…
Trillion edges benchmark: new world record beyond 100TB by TigerGraph featuring AMD based Amazon EC2 instances
Graph databases have become increasingly popular in recent years, as they are uniquely suited to handle complex, interconnected data. As data sets continue to grow, scaling up graph databases to…
The Beauty of Graph Algorithms with Built-in Parallelism
Many people already know that graph algorithms are the most efficient and sometimes the only solution for complex business use cases, such as clustering different groups of users (Community Detection),…
Enhanced Pattern Matching Query Syntax
In our upcoming 2.4 release, TigerGraph will offer a major syntax extension in GSQL - pattern matching. Pattern matching enables users to focus on specifying what multi-hop path pattern they…
Building a Graph Database on a Key-Value Store?
by Dr. Xu Yu, CEO and Dr. Victor Lee, Director of Product Management [Excerpted from the eBook Native Parallel Graphs: The Next Generation of Graph Database for Real-Time Deep Link…