TigerGraph, a Redwood City, California-based software developer providing a suite of enterprise graph database tools, today announced that that it’s secured $ 32 million in series B funding led by private equity firm SIG. The infusion of capital comes after a $ 31 million series A in September 2017 and nearly doubles the startup’s haul to $ 60 million, as it continues to attract marquee clients like Zillow, Intuit, Amgen, Wish, Kickdynamic, and China Mobile.
“Today’s vast amount of data, together with increasingly powerful processing capabilities enabled by the cloud, means it is now possible to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries,” said CEO and founder Yu Xu, a former IBM, Teradata, and Twitter engineer and systems architect who founded TigerGraph in 2011. “The funding will fuel a new wave of growth and expansion for TigerGraph to make deep link analysis accessible to virtually every organization in the world and help users unleash the power of interconnected data.”
Graph databases and graph-oriented databases leverage graph structures for semantic queries, with nodes, edges, and properties that store and represent data. They’re a type of non-relational technology that depicts the relationships connecting various entities (like two people in a social network, for instance) and that can analyze interconnected data.
TigerGraph says its cloud-hosted and pay-as-you-go service — which is now generally available — simplifies graph management and configuration organization-wide, even for departments lacking the technical prowess to produce graph databases from scratch. To this end, its three-step graph-generating tool ostensibly gets apps up and running within minutes to hours. Plus, TigerGraph delivers a dozen starter kits addressing use cases like fraud detection, personalized real-time recommendation, computation, explainable AI, machine learning, and supply chain analysis.
TigerGraph’s eponymous TigerGraph Cloud scales up to tens of terabytes, 100 billion vertices, and 600 billion edges on the high end. It can support with a single machine more than 100,000 real-time deep link analytics queries and 50GB to 150GB of data per second. On a cluster of 20 commodity machines, it’s capable of streaming over 2 billion daily events in real time.
TigerGraph’s SQL-like graph query language enables ad-hoc data exploration and analysis, while its architecture makes use of compression to minimize memory overhead. Graphs are structured such that vertices and edges act as parallel storage and computation units, each of which can hold any amount of arbitrary information. This allows TigerGraph Cloud to run multiple engines hosting graphs with different partitioning algorithms, queries to which a front-end server can automatically route based on type.
“At Kickdynamic we know that compelling, individualized experiences are the most effective way to create customer loyalty and drive revenue,” said Kickdynamic chief product officer Gabriele Corti. “Having tried various other solutions, we found that TigerGraph offered the best combination of performance and advanced, real-time, analytical capabilities. TigerGraph’s scalable graph database will enhance our platform and enable us to continue to achieve our vision of delivering advanced personalization in email.”
Markets and Markets anticipates the graph database market will reach $ 2.4 billion by 2023 from $ 821.8 million in 2018, and analysts at Gartner expect that enterprise graph processing and graph databases will grow 100% annually through 2022. Startups like Neo4j, MongoDB, Cambridge Semantics, DataStax, and others have risen to meet the need, in addition to incumbents like Microsoft and Oracle. Even Amazon threw its hat in the ring in November 2017 with the launch of Neptune, a fully managed graph database powered by its Amazon Web Services division.