TigerGraph announced new functionality and performance for TigerGraph Cloud, its distributed native graph database-as-a-service, is an intuitive way to build and run applications that work with highly connected and complex datasets.
TigerGraph’s latest distributed system and high availability enhancements help enterprises leverage advanced analytics on graph at scale, which requires larger and more varied dataset combinations, which means more variables and relationships to analyze, explore and test to make machine learning (ML) and artificial intelligence (AI) better.
“To survive and thrive in today’s business world, enterprises should accelerate their approach to AI and ML applications, both of which benefit from graph analytics, as this helps identify new patterns across the data sources, create new models and better algorithms, using data at scale,” said Todd Blaschka, COO, TigerGraph. “TigerGraph Cloud’s enhanced services provide our customers with an even better platform for analytical and transactional processing. Users can start for free and expand to a distributed production system as their applications scale. Starting a distributed graph system with high availability is simply choosing the number of machines and the number of replicas on TigerGraph Cloud.”
TigerGraph Cloud delivers on one promise: graph analytics is the way forward. Until now, organizations had to rely on data scientists, developers and architects to design their graph-based data analysis solutions. TigerGraph Cloud has addressed this innovation gap with an easy-to-use, cloud-based graph service that makes graph database and analytics accessible to everyone; the solution is the only distributed graph service with high availability.
TigerGraph Cloud provides the ideal cloud-based service to model, search, and traverse relationships for analytical, transactional and real-time workloads. Users can start for free, then as their data expands they can expand across the cloud. At the same time, with TigerGraph’s ability to do SQL-like database computation along with ACID-compliant transactions, users benefit with a lower TCO compared to other analytic products.
TigerGraph Cloud allows users to get started in minutes, build a proof-of-concept model in hours and deploy a solution to production in days. It eliminates the need to set up, configure or manage servers, schedule backups or look for security vulnerabilities. Also, TigerGraph offers a free tier of TigerGraph Cloud in perpetuity that enables data scientists, developers, business analysts, students and other enthusiasts to experience this technology’s unique power to handle real-world data challenges.
With this version of TigerGraph Cloud that includes configuration for distributed graphs and replica instances for high availability, as well as the ability to leverage EFS for backup and restore.
In addition to elastic, pay-only-for-what-you-use pricing, subscribers can provision distributed TigerGraph services for large production datasets; have the choice to deploy high availability TigerGraph services; and have more TigerGraph Cloud starter kits to choose from for fast application development, including new neural networks, cybersecurity and payment fraud detection kits. Starter kits are 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.
Going into 2020, Microsoft Azure will soon be one of the backend options for users of TigerGraph Cloud, making it the first truly cloud neutral graph database-as-a-service in the market.
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