TigerGraph Extends GSQL Enhancements
Graph analytics is moving closer to the industry-standard SQL framework for accessing relational databases via enhancements such as those in TigerGraph Inc.’s query language, GSQL.
Property graph query languages such as GSQL are built on top of SQL, bringing graph pattern matching capabilities to existing frameworks used to store and retrieve data. GSQL, the property graph query language touted as supporting data aggregation for graph analytics, will support an upcoming standard query language for graph databases, the company announced recently.
TigerGraph also said its latest version of GSQL extends current capabilities, making graph analytics easier to use. The goal is greater adoption of graph analytics by enterprise users already familiar with SQL.
TigerGraph, Redwood City, Calif., released it latest version of GSQL in March. It has since launched a multi-cloud graph database service on Microsoft Azure. It launched its first cloud database on Amazon Web Services in November 2018.
The startup said its multi-cloud database service targets enterprise users seeking to “model, search, and traverse relationships for analytical, transactional and real-time workloads.”
The 3.0 version also includes “no code” graph analytics tools designed to make it easier for data scientists and business analysts to move data from relational to graph databases. That capability would eliminate the need for complex queries that could instead be drawn using graph analytics to discern hidden patterns.
The company said GSQL extends SQL functionality via “accumulators,” a concept that accelerates complex computations on connected data sets. The latest version uses accumulators to aggregate data without writing code. TigerGraph’s implementation is also promoted as more powerful than traditional SQL aggregation functions.
GSQL’s aggregation support based on accumulators replaces SQL-style aggregation while enabling “single-pass” graph traversal queries, the company noted.
In a paper delivered during the SIGMOD 2020 conference, TigerGraph Chief Scientist Alin Deutsch outlined graph analytics’ advantages for analyzing connected data sets. The company also said GSQL will support the upcoming industry standard language for property graph querying. GQL is being readied by the working group that maintains the SQL standard.
As some graph database users begin to hit limits on what single-system graph databases can deliver, distributed property graph database startups such a TigerGraph have been able to make gains in the growing graph database sector.
Founded in 2012, TigerGraph emerged from stealth mode in 2017. According to the web site Crunchbase.com, TigerGraph has so far raised $66.7 million in venture funding.