TigerGraph review: A graph database designed for deep analytics

Highly parallelized and horizontally scalable, TigerGraph shines for use cases that require multi-hop analytic queries

At a Glance

Graph databases offer a more efficient way to model relationships and networks than relational (SQL) databases or other kinds of NoSQL databases (document, wide column, and so on). Lately many products have arisen in this space, which was originally (in 1999) the sole province of Neo4j.

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TigerGraph, a recent arrival, is a “real-time native parallel graph database.” TigerGraph is available for deployment in the cloud or on-premises, it scales both up and out, it automatically partitions a graph within a cluster, it’s ACID compliant, it has built-in data compression, and it claims to be faster than the competition. As we’ll see, it uses a message-passing architecture that is inherently parallel in a way that scales with the size of the data.

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