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Introducing TigerGraph, a Native Parallel Graph Database

  • Victor Lee
  • September 29, 2017
  • About TigerGraph, blog
  • Blog >
  • Introducing TigerGraph, a Native Parallel Graph Database

Yu Xu, TigerGraph CEO

By Yu Xu, TigerGraph founder and CEO

Today we proudly launch TigerGraph, a ground-breaking technology built from the ground up to address a critical pain point: the need for real-time analytics for enterprises with massive amounts of data.

I was first exposed to this need as a college student in China working with companies with massive amounts of data. My journey to find a solution to the problem brought me to the United States, where I received my Ph.D. from UC San Diego, followed by my work at Teradata and Twitter.

Graph databases offer considerable benefits, but solutions thus far have missed the mark in delivering upon the promise of graph analytics. Indeed, first generation graph databases were never designed to support current enterprise challenges of massive and complex big data.

TigerGraph was born to achieve this. Whereas other solutions are limited to traversing two hops in big data graph queries, TigerGraph provides a robust enterprise platform for Deep Link Graph Analytics (DLA) which requires three hops or more along with fast query response time.

How is TigerGraph able to do this? By offering the world’s first and only Native Parallel Graph (NPG) – a solution that focuses on both storage and compute to support real-time graph updates and built-in parallel computation. TigerGraph is a complete, fully distributed graph analytics platform. Its performance features include:

Real-Time Deep Link Query Speed.

The ability to traverse hundreds of million of vertices/edges per second per machine traversing three hops or more, orders of magnitude faster that traditional approaches.

Real-Time Graph Loading.

The ability to load 50 to 150 GB of data per hour per machine. No more batch loading!

Massive Scale.

The ability to stream 2B+ daily events in real-time to a graph with 100B+ vertices and 600B+ edges on an only 20 commodity machine cluster, battle-tested by the world’s largest e-payment company with over two years in production.

TigerGraph supports real-time analytics for the largest datasets, including: fraud prevention at the world’s largest e-commerce provider, recommendations at the world’s largest mobile e-commerce company, and network management at the world’s largest electric grid company. Our customers include brand-names such as State Grid Corporation of China, Wish.com and Elementum.

Along with our product, TigerGraph also today announced $31 million in Series A funding – one of the most sizeable financing rounds in graph database history, and the launch of our Cloud Service on Amazon EC2. To learn more, visit: www.tigergraph.com.

I look forward to continuing the TigerGraph journey and invite you to join us as we define the next stage in the graph database evolution.

Together we can help enterprises roar with the power of real-time analytics over their complex and massive amounts of data – providing competitive business advantages like never before.

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    Dr. Jay Yu

    Dr. Jay Yu | VP of Product and Innovation

    Dr. Jay Yu is the VP of Product and Innovation at TigerGraph, responsible for driving product strategy and roadmap, as well as fostering innovation in graph database engine and graph solutions. He is a proven hands-on full-stack innovator, strategic thinker, leader, and evangelist for new technology and product, with 25+ years of industry experience ranging from highly scalable distributed database engine company (Teradata), B2B e-commerce services startup, to consumer-facing financial applications company (Intuit). He received his PhD from the University of Wisconsin - Madison, where he specialized in large scale parallel database systems

    Todd Blaschka | COO

    Todd Blaschka is a veteran in the enterprise software industry. He is passionate about creating entirely new segments in data, analytics and AI, with the distinction of establishing graph analytics as a Gartner Top 10 Data & Analytics trend two years in a row. By fervently focusing on critical industry and customer challenges, the companies under Todd's leadership have delivered significant quantifiable results to the largest brands in the world through channel and solution sales approach. Prior to TigerGraph, Todd led go to market and customer experience functions at Clustrix (acquired by MariaDB), Dataguise and IBM.