Blog

1. Problem Description Enterprises know the benefits of merging data from multiple sources, to build more detailed and more complete records about their customers, their products, their employees, etc. The sources may be different departments or computer systems within one enterprise, a combination of internal and...

It’s that time of the year when 40,000+ people cram into Las Vegas for Amazon AWS re:Invent , which kicks off November 26. If you think about it, the attendees are all connected, but don’t realize it. It’s like a huge graph and many new insights and...

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 even 10-hop queries. Multi-hop queries on large data sets are the future of graph analytics. After...

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 TigerGraph. TigerGraph can effectively compresses the data size and needs 19.3x less storage space than Neo4j's. On...

What a difference a year makes. One year ago, we presented the public launch of TigerGraph 1.0, delivering the next stage in the evolution of graph database with the world’s fastest and most scalable graph platform. This came after five years in stealth developing state-of-the-art technology...

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 year, we published a benchmark report on Neo4j, Titan and TigerGraph. It is intriguing for...