TigerGraph has been updated with new availability, scalability, manageability, and security features that the team says will ensure mission-critical graph applications work flawlessly in both private and public clouds.

TigerGraph is a parallel graph database that is available as a graph database-as-a-service platform. The improvements to the new version start with features aimed at enterprise users, specifically through the ability to carry out cross-region replication of TigerGraph clusters, and simplified management for cluster resizing.

The developers say backup and restore is also faster, and you now have direct control over resource allocation for big queries.

TigerGraph has also passed the 30TB LDBC-SNB BI benchmark for 70+ billion nodes and 500+ billion edges; TigerGraph is the first and only commercial vendor to achieve this so far. The Linked Data Benchmark Council (LDBC) is a non-profit organization that aims to define standard graph benchmarks, and their Social Network Benchmark's Business Intelligence workload tests aggregation- and join-heavy complex queries touching a large portion of the graph with microbatches of insert/delete operations.

The new release also has built-in Kubernetes support for cloud management, and user-defined roles for security and access control at scale.

This release has a number of improvements aimed at making it more appealing to developers, with enhancements to the query language and faster query build performance. The query language has a further thirty built-in functions, along with flexible variable definition, flexible query function parameter assignment and query function return, and query function overloading. Batch queries have been improved so they're faster and more resilient for build and install, and the GraphStudio IDE now has WCAG (Web Content Accessibility Guidelines) compliant accessibility.

The new version also has a number of new features for analysts, with new machine learning capabilities within the TigerGraph In-Database Graph Data Science Library. The changes mean data scientists can access double the number of built-in graph algorithms, including new graph embedding algorithms such as Node2Vec and FastRP.

TigerGraph 3.2 is available now.

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