Graph databases are the fastest growing category in data management, according to DB-Engines. However, most graph queries only traverse two hops in big graphs due to limitations in most graph databases. Real-world applications require deep link analytics that traverse far more than three hops. To support real-time deep link analytics, we need the power to combine real-time data updates, big datasets, and deep link traversals.
Yu Xu offers an overview of TigerGraph’s distributed native parallel graph, a fraud detection system that manages 100 billion graph elements to detect risk and fraudulent groups. Yu discusses the techniques behind the distributed native parallel graph platform, including how it partitions graph data across machines, supports fast updates, and is still able to perform fast graph traversal and computation. He also shares a subsecond real-time fraud detection system managing 100 billion graph elements to detect risk and fraudulent groups.
Yu Xu is the founder and CEO of TigerGraph, the world’s first native parallel graph database. He is an expert in big data and parallel database systems and has over 26 patents in parallel data management and optimization. Previously, Yu worked on Twitter’s data infrastructure for massive data analytics and was Teradata’s Hadoop architect leading the company’s big data initiatives. Yu holds a PhD in computer science and engineering from the University of California, San Diego.
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Appreciate the in depth overview. Do you happen to have a link to slides? I’ll search some more