TigerGraph, provider of an advanced analytics and ML platform for connected data, announced the latest version (3.9) of TigerGraph Cloud, a native parallel graph database-as-a-service, including new security, advanced AI, and machine learning capabilities to streamline the adoption, deployment, and management of the graph database platform. The underlying parallel native graph database engine is also available for on-prem or self-managed cloud installation.
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. Users can get started in minutes, build a proof-of-concept model in hours, and deploy a solution to production in days. New capabilities include: Enhanced data ingestion, Parquet file format, multi-edge support, Enhanced graph data science package, improved DevOps support, expanded Kubernetes functionality, and expanded self-service graph visual analytics
TigerGraph Cloud users can choose from 20+ starter kits that cover industry use cases pre-built with sample graph data schema, dataset, and queries focused on specific use cases such as fraud detection, real-time recommendation, machine learning, and explainable AI.
