Leveraging Geospatial Data with a Native Parallel Graph Database
Paradigm Shift Enables TigerGraph to Uncover Hidden Insights from Geolocation Data Have you wondered how you can add and leverage geospatial data to your applications? There is a massive amount…
On “Benchmarking RedisGraph 1.0”
Recently RedisGraph published a blog [1], comparing their performance to that of TigerGraph's, following the tests [2] in TigerGraph's benchmark report [3], which requires solid performance on 3-hop, 6-hop, and…
Building a Graph Database on a Key-Value Store?
by Dr. Xu Yu, CEO and Dr. Victor Lee, Director of Product Management [Excerpted from the eBook Native Parallel Graphs: The Next Generation of Graph Database for Real-Time Deep Link…
TigerGraph Roars Past The Competition: Graph Database Benchmark Findings
With the graph database market on the rise, it is important to understand the differentiators between each vendor. TigerGraph is the first and only native parallel graph with massively parallel…
Half-Terabyte Benchmark Neo4j vs. TigerGraph
Neo4j's loading time is shorter than TigerGraph; however, Neo4j requires extra preprocessing that extracts the vertex file from the edge file. After including the pre-processing time, Neo4j takes longer time to loading than…
Amazon Neptune, the Truth Revealed
In May this year, Amazon announced the General Availability of its cloud graph database service called Amazon Neptune. Here is a comprehensive blog that summarizes its strengths and weaknesses. Last…
It Is Time for A Modern Graph Query Language
The time is ripe for an international standard graph query language. Industry vendors including Neo4j have called this out, and we at TigerGraph wholeheartedly agree. As graphs continue to see…