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
For more information about this news, visit www.tigergraph.com.