The World’s Fastest and Most Scalable Graph Platform
Through its Native Parallel Graph™ technology, the TigerGraph™ graph platform represents what’s next in the graph database evolution: a complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time.
Combining the best ideas (MapReduce, Massively Parallel Processing, and fast data compression/decompression) with fresh development, TigerGraph delivers what you’ve been waiting for: the speed, scalability, and deep exploration/querying capability to extract more business value from your data.
Ability to traverse hundreds of millions of vertices/edges per second per machine.
Ability to load 50 to 150 GB of data per hour, per machine.
Ability to stream 2B+ daily events in real-time to a graph with 100B+ vertices and 600B+ edges on a cluster of only 20 commodity machines, battle-tested by the world’s largest e-payment company with over two years in production.
“We quickly ran into problems scaling with our original graph database...it’s huge data and finding inferences in that data is not easy, but TigerGraph has scaled for us.”
Vishnu Maddileti Data Science & Analytics
“We have been using TigerGraph for two years now at Wish. TigerGraph's speed, scalability, and graph model have enabled many applications for us that we previously thought were overly challenging.”
Jack Xie Head of Data at Wish
“Having tried various other solutions, we found that TigerGraph offered the best combination of performance and advanced, real-time, analytical capabilities. TigerGraph’s scalable graph database will enhance our platform and enable us to continue to achieve our vision of delivering advanced personalization in email.”
Gabriele Corti Chief Product Officer at Kickdynamic
“OpenCorporates is dedicated to making information on companies more usable and widely available for the public benefit, particularly to bring to light instances of criminal or anti-social activity - such as corruption, money laundering, and organized crime. As our work continues and our data grows, we had challenges scaling our data to meet our business needs. TigerGraph’s excellent scalability and performance enables us to achieve things we previously could not do, and to better support ongoing investigative work in the process.”
Chris Taggart CEO, OpenCorporates
“The electric power grid is a ‘physical world’ graph consisting of power generators, transformers, transmission lines, switches, meters, which is constantly changing. A real-time graph engine is essential to manage equipment in the grid and dynamically compute and estimate the electric power flowing in the grid for safety, efficiency, and operations planning. We chose TigerGraph for three reasons: its real-time high performance computational power, its scalability to process large graphs, and its flexible and powerful SDK which enables my teams to develop vertical applications quickly and efficiently.”
Dr. Guangyi Liu CTO, GEIRI North America, State Grid Corporation of China
"The question that graph databases answer are hard to come to conclusion in RDBMS or it takes forever. We needed a better tool to find relationships and TigerGraph was just that."
Ely Turkenitz IS Manager
"TigerGraph is the only system today that can help us make real-time care-path recommendations using knowledge of 50 million patients. Your products will have worldwide impact on making everyone’s lives better in more ways than you can imagine.”
Distinguished Engineer at a Fortune 10 Healthcare Company
“IceKredit considered several solutions, including Neo4j, to power our machine learning and AI applications. We selected TigerGraph for its superior data warehousing speed and computational processing capacity, which improved performance by an order of magnitude. The result is a drastic enhancement of our response speeds as we deliver credit evaluations to our customers.”
Dr. Lingyu Gu CEO, IceKredit
“TigerGraph differentiates itself by its ability to handle highly scaled graph computing problems through a unique distributed processing approach. Its massively parallel processing (MPP)-designed database can handle complex queries, traversing multiple links or hops, without having to break the problem and data down and execute in in separate runs.”