TigerGraph on Wednesday unveiled support for openCypher, a query language pioneered by competitor Neo4j.
Founded in 2012 and based in Redwood City, Calif., TigerGraph is a database vendor whose platform is based on graph technology that enables data points in a database to simultaneously connect with multiple other data points, rather than only one other data point at a time as in a relational database.
By connecting multiple data points at the same time, users often can more quickly and easily discover relationships between data points. As a result, common applications for graph databases include fraud detection, supply chain management and social networking. For example, Facebook and LinkedIn both use graph technology.
TigerGraph enables users to query their data using a query language the vendor developed called GSQL, a language that uses syntax similar to SQL in an attempt to make it easy to learn for coders with knowledge of the popular query language.
Now, TigerGraph is planning to add support for openCypher, which was originally developed by rival graph database vendor Neo4j and is now an open source project.
Support for openCypher is currently in preview, but any TigerGraph user can partake in the preview. General availability will be part of TigerGraph's next platform update, expected during the first half of 2023, according to the vendor.
The move comes as graph database vendors work in concert to develop a common query language called Graph Query Language (GQL), which will include aspects of openCypher and GSQL -- and others -- and is expected to become the standard query language for graph databases in 2024.
TigerGraph's support for openCypher, meanwhile, is significant, according to Matt Aslett, analyst at Ventana Research. It shows that the vendor is embracing openness by enabling customers to use a language associated with a rival, and that is supportive of the move to develop a standard query language that will give graph database users more choice.
Matt AslettAnalyst, Ventana Research
"TigerGraph's support for openCypher is ... illustrative of increased standardization in the graph data and analytics space as the industry moves toward GQL, [which] will be to the benefit of graph database users in providing greater choice," Aslett said.
TigerGraph's support for openCypher works by automatically translating code written in openCypher to GSQL.
When a user familiar with openCypher but not GSQL writes a query on TigerGraph with openCypher, TigerGraph will automatically translate the query to GSQL and run the query as though it had been written in GSQL from the start.
The result is that TigerGraph is now accessible to more potential customers than when it supported only its own query language.
The vendor currently has more than 120 enterprise customers, according to Jay Yu, TigerGraph's vice president of product innovation. Support for more query languages has the potential to enable TigerGraph to expand that number more quickly than it would have otherwise, even with a standard query language on the horizon.
"There are still a lot of developers out there who started with openCypher from Neo4j, and we wanted to reach out to them to lower the barrier to learning TigerGraph," Yu said. "Because of syntax differences, we were blocking many people from coming to us, which is why we decided to put openCypher into our GSQL language."
In addition to the potential new customers that the support for openCypher and other query languages might attract, existing TigerGraph customers requested that the vendor add support for openCypher. That was part of the reason TigerGraph made the move now rather than wait for GQL to be completed, according to COO Todd Blaschka.
Many of those existing customers are already familiar with openCypher from using Neo4j and open source tools. And they want to be able to use the query language they already know to work with TigerGraph, rather than go through the painstaking process of learning a new language.
"Developers get to know a language, but as they want to expand to more use cases and take advantage of newer data processing and data technology platforms ... we've been asked to support other languages so they can leverage their skill set," Blaschka said.
Listening to the wants of developers and data scientists is important, according to Aslett.
He noted that graph database use remains small compared with the use of relational databases, with only about 15% of companies Ventana surveyed using graph technology. However, that percentage is expected to grow another 20% over the next two years, according to Ventana, while Gartner predicts that graph technology will grow to account for 80% of data and analytics innovations by 2025.
That means opportunity exists for TigerGraph and rivals that offer graph databases, including Neo4j and tech giants such as Oracle and AWS, to substantially grow their customer base.
"To fuel adoption, graph database vendors have been stepping up their engagement with key personas -- including developers and data scientists -- who are exerting increasing influence over databases selected to support new applications," Aslett said. "TigerGraph recognized that it needed to ... facilitate them using the tools and skills used by data scientists today [with] graph databases."
As TigerGraph grows, both adding more customers and adding new capabilities, one of its main areas of focus will be developing a more complete platform for data science, according to Blaschka.
TigerGraph has built a solid tool for querying data, but connecting that querying capability to a larger ecosystem for data science -- and integrating it within that ecosystem -- is something the vendor is still working on.
"That is the next trend we're working on," Blaschka said. "We're focusing on building out the infrastructure, the integrations, the connectors to be part of an ecosystem for a data science experience."
In addition, the vendor is planning to add more pre-built applications to a cache that already includes fraud, supply chain management and customer 360.
"Cybersecurity is an area we're investing in," Blaschka said. "That's looking at anomaly detection, finding patterns and insights across a network."
Improved data visualization capabilities and more ease of use are other areas of focus, according to Yu.
Aslett, meanwhile, noted that some of TigerGraph's strengths are its pre-built applications and focus on specific applications for graph technology. However, he too said the vendor needs to hone its platform to meet the needs of data scientists.
"TigerGraph's focus on data scientists is a work in progress, as is its embrace of cloud architecture and managed services," he said. "Both stand to gain from further research and development."