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Its native parallel graph technology (NPG) powers real-time deep link analytics for enterprises trying to graph and process really Big Data. It’s touting it as the only system on the market to unify real-time analytics with large-scale offline data processing for graphs.
TigerGraph’s security feature makes it simple for departments to define access to specific areas of a data set. This is achieved through the creation of subgraphs representing a subset of the vertices and edges in a graph. Each subgraph has its own administrator, can overlap with other subgraphs as needed, and can be treated in the same way as one, standalone physical graph database. Benefits include less effort and cost for administrators, who are able to support controlled data access across departments — all with only one physical cluster system to manage.
TigerGraph is bringing to market a third-generation graph database technology that uses massive parallelization to offer high performance, real-time computation. In addition, it offers ‘deep link analytics,’ which means it can traverse hundreds of millions of vertices/edges per second per machine across a complex graph structure, empowering deeper, faster processing of graph-based data than any of its competitors.
As part of today’s launch, TigerGraph announced that it received $31 million in Series A funding, which Xu claims makes the company the second most well-funded graph startup after Neo4j (formerly Neo Technologies). The company also announced the availability of TigerGraph 1.0, as well as the launch of TigerGraph Cloud, a hosted version of the database that resides on Amazon Web Services. It also formally changed its name from SQLGraph.
TigerGraph founder and CEO Yu Xu is no stranger to the challenges of building distributed computational engines. After getting his PhD in distributed databases from University of California at San Diego, he went up the street to Teradata, where he led the MPP (massively parallel processing) database team and also worked on big data projects. Then Xu headed off to Twitter, where he helped built the social media company’s distributed data infrastructure.
TigerGraph Inc., formerly known as GraphSQL Inc., said it has built the first native parallel graph database-based platform using proprietary technology that the company says juices performance up to 100-fold compared with other graph platforms.
TigerGraph, a Redwood City, Calif.-based real-time graph analytics platform, raised $31 million in Series A funding. Investors include Qiming VC, Baidu, Ant Financial, AME Cloud, Morado Ventures, Zod Nazem, Danhua Capital and DCVC.
Redwood City-based TigerGraph, a developer of database software used for data analytics, said this morning that it has raised $31M in a Series A funding. The funding came from Qiming VC, Baidu, Ant Financial, AME Cloud, Morado Ventures, Zod Nazem, Danhua Capital and DCVC. The company–formerly known as GraphSQL–is led by Yu Xu.
The company says it has developed a complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time.
TigerGraph has made announcements including its emergence from stealth, securing of $31M in Series A funding, general availability of TigerGraph, and availability of both its Cloud Service and GraphStudio.
Formerly known as GraphSQL while in stealth, TigerGraph is a technical breakthrough representing the next stage in the graph database evolution – a complete, distributed, parallel graph computing platform supporting web-scale data
A startup named TigerGraph emerged from stealth today with a new native parallel graph database that its founder thinks can shake up the analytics market. With $31 million in venture funding and several high-profile customers, he might just be right.