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.
General advantages of the NPG include:
Faster data loading speed to build and update graphs.
Subsecond queries of Deep Link Analytics.
Support for large graphs from 100 million to 100+ billion vertices.
Ability to unify real-time analytics with large scale offline data processing.
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.
Outstanding performance in industry, surprising fast loading speed, efficient use of disk and memory, no noticeable delay in exploration in GraphStudio and GSQL is easy to learn!
Just installed TigerGraph developer edition: it is very easy to setup a new stand-alone environment. Now it's time to learn more about this powerful graph database.
There are many interesting applications which can greatly benefit from a graph database now, and in the coming years. The key challenge is to have a distributed graph database that can handle large amount of graph data and efficiently parallelize processing for speed. TigerGraph has unique technology that delivers today.
TigerGraph is the first graph processing system that makes a serious attempt to store the data in native graph form that is suited for parallel processing. It carries out this vision throughout the entire implementation stack, building everything with a view toward efficient parallel processing. We are in the age of big data. We’re in the age of huge graphs that no longer fit in a single machine, or if they do, they are large enough that computation over them is not efficiently carried out on a single machine. We need to parallelize this computation. In order to do that, we need to start from a platform that is already sensitive to the issues arriving from parallelization.
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.
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.