Latest TigerGraph Release Adds More Than 40 Features to its Graph Database

TigerGraph is releasing TigerGraph 3.2, including more than 40 critical enterprise features such as new availability, scalability, manageability, and security functionalities to ensure mission-critical graph applications work in both private and public clouds.

This latest enterprise version of TigerGraph will meet ever-increasing demand from the world’s top companies, boost developer adoption, and address key data science requirements, according to the vendor.

TigerGraph 3.2 includes the following new enterprise-grade capabilities:

  • Business continuity support via cross-region replication of TigerGraph clusters
  • Demonstrated scale via the 30TB LDBC-SNB BI benchmark; TigerGraph is the first and only commercial vendor to achieve this designation with 70+ billion nodes and 500+ billion edges
  • Simplified management via cluster resizing, faster backup and restore, and direct control over resource allocation for big queries
  • State-of-art cloud management via built-in Kubernetes support
  • Security and access control at scale via user-defined roles

TigerGraph is democratizing the adoption of advanced analytics by making graph accessible and available to more organizations, empowering business users and data scientists to go 10+ levels deep into data, in real-time, across billions of relationships.

The new 3.2 release will increase developer adoption with new features that contribute to a more productive developer experience via accessibility compliance, query language enhancement, and query build performance speedup.

These developer-friendly capabilities include:

  • WCAG compliant accessibility in GraphStudio
  • Enhanced query language features via 30+ more built-in functions, flexible variable definition, flexible query function parameter assignment, flexible query function return, and query function overloading
  • Faster and more resilient batch queries for build and install

The TigerGraph in-database graph data science library has key advantages over other offerings:

  • Algorithms run in the database, meaning there is no need to copy the database, and algorithms run on the latest data, not a stale copy
  • Database scalability, as TigerGraph is a distributed, scalable database, running as one unit up to tens and hundreds of terabytes
  • Massively parallel processing, as algorithms are compute-intensive and parallelizable, so having a graph engine that can take advantage of that potential is a huge advantage to the user
  • An all open-source current library using the same GSQL query language and graph engine used for user-authored queries, meaning no challenges with approximation algorithms or partial results and the ability to customize TigerGraph data science algorithms

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