Q1. How is TigerGraph different then other existing Graphs Databases?
Traditional solutions are not designed to support the massive data volumes and data creation rates enterprises face today, and are unable to provide the full benefits of graph analytics. In turn, they fail to deliver the high performance deep link analytics needed to power enterprise applications. Through its Native Parallel Graph™ technology, TigerGraph represents what’s next in the graph database evolution: a complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time. TigerGraph offers the speed, scalability, and deep exploration/querying capability to extract more business value from data.
TigerGraph is especially suited for very large graphs – the best model for deep link analytics as they enable exploration, discovery and prediction of relationships.
These features are essential enterprise applications including: personalized recommendations, fraud prevention, supply-chain logistics optimization, company knowledge graph and more.
Q2, Can you tell us a bit more what is TigerGraph’s Native Parallel Graph Technology (NPG), and what is it useful for?
TigerGraph’s Native Parallel Graph™ (NPG) design focuses on both storage and computation, supporting real-time graph updates and offering built-in parallel computation. The NPG serves today’s data-driven businesses to create more intelligent applications and services. General advantages of the NPG include:
Fast data loading speed to build graph,
Fast execution of parallel graph algorithms,
Real-time capability for streaming updates and inserts using REST,
Ability to unify real-time analytics with large-scale offline data processing.
Q3. What is your roadmap ahead?
We continue to co-innovate with our customers. We released a complete web-broswer based Visual SDK called GraphStudio in Sept 2017. We’re continuing to add more functionalities to GraphStudio. At the query language level, we’ll introduce some new powerful features which will further simplify developer efforts in geospatial use cases. Also we’re adding more vertical solutions on top of our graph platform including routing/shipment optimization and banking fraud solutions.
Dr. Yu Xu is the founder and CEO of TigerGraph, the world’s first native parallel graph database. Dr. Xu received his Ph.D in Computer Science and Engineering from the University of California San Diego. He is an expert in big data and parallel database systems and has 26 patents in parallel data management and optimization. Prior to founding TigerGraph, Dr. Xu worked on Twitter’s data infrastructure for massive data analytics. Before that, he worked as Teradata’s Hadoop architect where he led the company’s big data initiatives.