TigerGraph

Overview
HQ Location
United States
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Year Founded
2012
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Company Type
Private
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Revenue
< $10m
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Employees
51 - 200
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Website
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Twitter Handle
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Company Description
TigerGraph is a provider of a graph database platform for enterprise applications. It fulfills the true promise and benefits of the graph platform by supporting real-time deep link analytics for enterprises with complex and colossal amounts of data. TigerGraph???s proven technology is used by customers including Alipay, VISA, SoftBank, State Grid Corporation of China, Wish and Elementum. The company supports applications such as IoT, AI, and Machine Learning to make sense of ever-changing big data. It also provides personalized recommendations, fraud prevention, supply-chain logistics, company knowledge graph, and other features. Founded by Yu Xu, Ph.D. in 2012, TigerGraph is funded by Qiming VC, Baidu, Ant Financial, AME Cloud, Morado Ventures, Zod Nazem, Danhua Capital, and DCVC. TigerGraph is based in Redwood City, CA.
IoT Solutions
TigerGraphDb
TigerGraph is delivering the next stage in the evolution of the graph database: the first system capable of real-time analytics on web-scale data. Our Native Parallel Graph? (NPG) design focuses on both storage and computation, supporting real-time graph updates and offering built-in parallel computation. Our SQL-like graph query language (GSQL) provides for ad-hoc exploration and interactive analysis of Big Data. With GSQL’s expressive capabilities and NPG speed, you’ll be able to perform Deep Link Analytics: uncovering connections that previously were too impractical to reach or too cumbersome to express.
TigerGraph Cloud
Built for agile teams who’d rather be building innovative applications to deliver new insights than managing databases. Start in minutes, Build in Hours and Deploy in Days with a TigerGraph Cloud graph database as a service.
TigerGraph GraphStudio?
TigerGraph GraphStudio? is our simple yet powerful graphical user interface. GraphStudio integrates all the phases of graph data analytics into one easy-to-use graphical user interface. GraphStudio is great for ad-hoc, interactive analytics and for learning to use the TigerGraph platform.
TigerGraph is delivering the next stage in the evolution of the graph database: the first system capable of real-time analytics on web-scale data. Our Native Parallel Graph? (NPG) design focuses on both storage and computation, supporting real-time graph updates and offering built-in parallel computation. Our SQL-like graph query language (GSQL) provides for ad-hoc exploration and interactive analysis of Big Data. With GSQL’s expressive capabilities and NPG speed, you’ll be able to perform Deep Link Analytics: uncovering connections that previously were too impractical to reach or too cumbersome to express.
TigerGraph Cloud
Built for agile teams who’d rather be building innovative applications to deliver new insights than managing databases. Start in minutes, Build in Hours and Deploy in Days with a TigerGraph Cloud graph database as a service.
TigerGraph GraphStudio?
TigerGraph GraphStudio? is our simple yet powerful graphical user interface. GraphStudio integrates all the phases of graph data analytics into one easy-to-use graphical user interface. GraphStudio is great for ad-hoc, interactive analytics and for learning to use the TigerGraph platform.
Key Customers
State Grid, Amgen, County Of Santa Clara, OpenCorporate, Pagantis
IoT Snapshot
TigerGraph is a provider of Industrial IoT infrastructure as a service (iaas), and analytics and modeling technologies.
Technology Stack
TigerGraph’s Technology Stack maps TigerGraph’s participation in the infrastructure as a service (iaas), and analytics and modeling IoT Technology stack.
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Devices Layer
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Edge Layer
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Cloud Layer
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Application Layer
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Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
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Case Studies.

Case Study
Improves Customer Experiences
This company was looking to build a new core customer 360 record system which would offer a product recommendation system and entity resolution feature. The new product recommendation system would enable the company to create accurate customer profiles that showed hierarchical relationships and help deliver an exceptional customer experience when customers log in to their centralized database to perform functions, buy products, request services, etc.They also wanted it to be scalable and a more performant system than their last. This operational function for their customer 360 was critical to their competitive advantage in the market. The system would consist of a centralized data source that would be the brains of the customer data platform.

Case Study
Major Financial Institution Improves Its Ability to Combat Money Laundering
The financial institution was looking to improve its networking and link analysis capability for anti-money laundering to be applied in three ways:Connections between open work items in situations of interest (such as previous SAR filings, and other open work items) should be identified and available to analysts and investigators;Thorough ad hoc reviews of an entity should display the connections from an ecosystem surrounding a specific starting point of an investigation;The system should enable analysts to identify which connections and situations of interest lead to productive investigations and inform the creation, hibernation, or escalation of work items.The company examined a number of alternatives in the hope of finding one that offered a client-focused approach, state-of-art technology, and next-generation database management solution that integrated seamlessly with its existing workflow.
Case Study
Market Leader in Cyber Resilience Scales Next-Generation Cybersecurity Services
The cybersecurity company was unable to scale their classification services with their existing solution based on SQL Server. New websites emerge at astonishing rates – in order to deliver effective cybersecurity, they needed to use accurate and timely threat data and execute thousands of classifications per second across massive data sets.They recognized that they needed an entirely new backend to power their classification services in order to keep up with the ever-expanding internet.
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