An improved version of TigerGraph Cloud, the parallel graph database has been launched as a graph database-as-a-service platform.

TigerGraph Cloud was announced a year ago, but only became generally available in September 2019. The developers have now announced improvements including better performance and 'more robustness'.

TigerGraph's developers say it offers the fastest graphics analytics implementation in its enterprise platform. The founder of TigerGraph, Yu Xu, led the massively parallel programming (MPP) team at Teradata before moving to Twitter. He then left to design a faster graph database that was natively distributed. Some other graph databases store data on top of Hadoop or NoSQL, though Neo4j, the current leader in the graph database market, stores its data in a graph structure with the data as nodes and the connections between data as edges.

Where TigerGraph differs is that it starts from a parallel model rather than adding it later, making more complex graph analytics possible alongside the ability to deal with much larger amounts of data. TigerGraph can run on more than 1,000 nodes of commodity Intel processors, meaning it can run queries with up to 20 hops without losing performance.

The latest release includes configuration for distributed graphs and replica instances for high availability, as well as the ability to use EFS for backup and restore. There are also more TigerGraph Cloud starter kits to choose from, including new neural networks, cybersecurity and payment fraud detection kits. Starter kits are built with sample graph data schema, dataset, and queries.

The developers also say that they are planning to add Microsoft Azure as one of the backend options for users of TigerGraph Cloud, making it more cloud neutral.

TigerGraph offers a free tier of TigerGraph Cloud in perpetuity that data scientists, developers, business analysts, students and other enthusiasts can use.

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TigerGraph

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