This paper discusses TigerGraph, a native graph parallel database that has been designed specifically to support real-time (less than one second) analytics. The keys to achieving this are parallelism, compression and the way that, in TigerGraph, graph edges and vertices are not just units of storage but also computational units.
It is available both in on-premises and cloud (AWS and Azure) versions. The company has also announced TigerGraph Cloud through which the product will be available as a service. TigerGraph uses a property graph paradigm and its strengths are with processing structured rather than semantically oriented data. Its main areas of focus are anti-fraud, customer intelligence, supply chain intelligence and energy efficient analytics.
Author/s: Philip Howard,Daniel Howard