TigerGraph Inc. today updated TigerGraph Cloud, its fully managed graph database service, with new capabilities that will enable users to extract more insights from their business data and accelerate the development of machine learning applications.
The company is a well-funded database startup that has raised more than $170 million to date, closing on its most recent round of funding in February 2021. Its customers include major enterprises such as JPMorgan Chase & Co. and Microsoft Corp.
TigerGraph is the developer of an industry-leading graph database that’s designed to perform certain tasks more efficiently than relational and NoSQL database systems can do. For example, there are many situations where databases are required to store not only business records, but also contextual information about how those records are connected to each other.
A sales database might be required to specify the store where each sale took place, for instance. With traditional databases, these connections between records are difficult to analyze, because they don’t always provide an easy way to identify if two data points are linked in some way. As a result, finding connections between different data points requires significant processing power.
That’s where TigerGraph comes in handy with its graph database, which can store both records and the connections between them. As a result, users can perform rapid analysis and queries, helping to speed up a range of applications that need to map connections between data points. Examples of these applications include machine learning and cybersecurity.
The new capabilities announced in TigerGraph Cloud include TigerGraph Insights, which is an intuitive, no-code visual graph analytics tool that can help users to better explore these connections and derive more useful insights from them. More specifically, TigerGraph said, users can create interactive visual representations of business intelligence applications that run on TigerGraph Cloud.
The tool works by connecting intuitive graph data with traditional BI tools to produce interactive, multidimensional graphics visualizations. Users can then link these graphics together to create tables, charts and maps that visualize graph stories. What’s more, that can be done without any coding, using a simple point-and-click, drag-and-drop interface.
Also new is ML Workbench, which is a Python-based framework that’s meant to help accelerate the development of graph-powered machine learning applications. It’s aimed at data scientists and can help them to improve the accuracy of machine learning models, shorten development cycles and bring greater business value to the table faster, the company said.
That can be done using familiar tools, workflows and libraries within a single environment that plugs straight into TigerGraph’s existing data pipelines. From there, users can launch high-performance graph feature generation, sampling and training that’s powered by TigerGraph’s massive parallel graph data compute engine, with access to more than 55 open-source graph algorithms, the company said. The resulting graph features can then be extracted and converted into efficient data formats for use by downstream neural networks.
Jay Yu, TigerGraph’s vice president of product and innovation, said the company has long been committed to democratizing graph databases and pushing the limits of innovation. “The addition of visual graph analytics and machine learning tools to our fully managed graph database-as-a-service offering lowers the barrier to graph entry even further,” Yu said. “Now, enterprises of all sizes can supercharge their data analytics and machine learning projects at scale with speed, asking and answering critical business questions that move the needle.”
TigerGraph said the new features are available now in the TigerGraph Cloud 3.8 release, and can be accessed via TigerGraph Suite, which is a single, streamlined web portal that enables frictionless collaboration between graph users. TigerGraph Suite incorporates the full range of TigerGraph Cloud’s services, including GraphStudio, GSQL Shell, GraphQL Gateway and AdminPortal, and is available on Amazon Web Services, Microsoft Azure and Google Cloud.