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.
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.
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.
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.
We tried many Graph databases but none met our requirements because of slow loading speed or slow query performance. TigerGraph’s super fast data loading speed and real-time sub-second query performance on large datasets provides unparalleled performance advantages. It's a tool that really lets you tap into the full benefits of a graph analytics platform.
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.