Big Graphs

TigerGraph produces a graph analytics platform for developers to create their own big data graph applications. This technology stores all data sources in a single, unified multiple-graph store that can scale out and up to explore, discover and predict relationships. Unlike traditional graph databases, TigerGraph can...

Prelude In December, I wrote a Quora post on the pros and cons of graph databases. I shared two cons pervasive in the market today: the difficulty of finding proficient graph developers, and how non-standardization on a graph query language is slowing down enterprise adoption, especially...

You know a technology has reached a tipping point when your kids ask about it. This happened recently when my eighth grade daughter asked, “What is Machine Learning and why is it so important?”. Answering her question, I explained how Machine Learning is part of AI,...

TigerGraph is providing the next evolutionary step in Graph Databases. It is the first system capable of performing Real-Time Analytics of data on a web-scale. The Native Parallel Graph (NPG) is designed to focus on both computation and storage, while supporting graph updates in real-time...

Real-time big graphs represent the next stage in the graph database evolution, and are designed to deal with massive data volumes and data creation rates to provide real-time analytics. Enterprises demand real-time graph analytic capabilities that can explore, discover and predict very complex relationships. This represents...

Dr. Yu Xu: Today, companies are demanding real-time data to make informed decisions and to provide better customer experiences. Graph analytics are optimized to deliver new insight and intelligence previously impossible or hard to detect, allowing enterprises to capture key business moments for competitive advantage. When...