TigerGraph Introduces Powerful New Capabilities to Streamline the Adoption of Graph Technology

TigerGraph, provider of an advanced analytics and ML platform for connected data, is releasing the latest version (3.9) of TigerGraph Cloud, the native parallel graph database-as-a-service.

TigerGraph Cloud 3.9 includes new security, advanced AI, and machine learning capabilities that meet the demands of its rapidly growing customer base and streamline the adoption, deployment, and management of the most scalable graph database platform, according to the company. The underlying parallel native graph database engine is also available for on-prem or self-managed cloud installation.

“Graph is a crucial tool for solving business challenges and TigerGraph is committed to helping customers unlock the full potential of their data by using ML and AI to close the gap between data and decisions,” said Jay Yu, vice president of product and innovation at TigerGraph. “Based on direct feedback from enterprise customers relying on TigerGraph to power mission-critical graph applications, this new release offers more advanced machine learning capabilities that allows customers to supercharge their data analytics projects at scale, with speed, and in the most collaborative way possible.”

Available as self-managed enterprise or on fully-managed cloud services including Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, TigerGraph Cloud equips users with a comprehensive, streamlined approach to deploy and maintain multiple graph database solutions with visual analytics and machine learning tools. New capabilities include:

  • Enhanced data ingestion: Simplified streaming data ingestion setup and support for the popular Parquet data format with enhanced progress monitoring and messages.
  • Parquet file format: Added support for the de facto open source storage format for big data as a data source.
  • Multi-edge support: Ability to allow multiple edges of the same type to exist between two vertices, simplifying the support of time-series and many other use cases.
  • Enhanced graph data science package: Achieve more scalable graph embedding with NodePiece and pyTigerGraph support for TigerGraph's packaged algorithms with just-in-time compilation.
  • Improved DevOps support: Access to detailed operational information, visually displayed by the Admin Portal; monitor individual queries and real-time status of each TigerGraph service and its dependencies.
  • Expanded Kubernetes functionality: Access to operator support for backup, cluster expand/shrink.
  • Expanded self-service graph visual analytics: Improved productivity via collaborative editing and viewing capabilities on shared visual graph dashboards.

In addition to new features, this release incorporates findings from several large scale deployments of TigerGraph Cloud to offer the highest level of product stability and security to ensure ease of use for TigerGraph customers, according to the vendor.

TigerGraph Cloud users can choose from 20-plus starter kits that cover real-world industry use cases such as customer 360, fraud detection, supply chain analysis, cybersecurity, and more. Starter kits are pre-built with sample graph data schema, dataset, and queries focused on specific use cases such as fraud detection, real-time recommendation, machine learning, explainable AI, and more.

For more information about this release, visit