Press
Press
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Graph analytics is an ideal technology to support AML. Graphs overcome the challenge of uncovering the relationships in massive, complex and interconnect data. The graph model is designed from the ground up to treat relationships as first-class citizens. This provides a structure that natively embraces and maps data relationships, even in high volumes of data, and provides maximum insight into data connections and relationships.
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TigerGraph Offers Graph Database Test Drive
TigerGraph, the startup that emerged last fall with a new native parallel graph database, released a free “developer edition” this week with the goal of giving potential users a test drive and a means of comparing the graph analytics platform with competitors like Amazon Neptune and Neo4j.
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SD Times news digest: BrowserStack’s open-source program, Tableau acquires AI startup, and TigerGraph’s free graph database
TigerGraph has announced the free developer edition of its graph analytics platform. The platform features enterprise graph massively parallel processing, support for Big Data, ability to write high-performance complex analytics queries, and the ability to continuous load over 100 GB per machine per hour.
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TigerGraph Announces Free Developer Edition of Graph Database
TigerGraph produces a graph analytics platform for developers to create their own big data graph applications.
This technology stores all data sources in a single, unified multiple-graph store that can scale out and up to explore, discover and predict relationships. Unlike traditional graph databases, TigerGraph can scale real-time multi-hop queries to trillions of relationships.
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Open or closed? On graph database access, query languages, community building, and TigerGraph
If you’ve been watching the graph database space, you probably know TigerGraph. It’s one of the latest entries, coming out of stealth in September 2017. TigerGraph has managed to get attention in a number of ways: its massively parallel architecture, the benchmarks it has released, the names of its clients, and its strong presence in industry events.
“As graphs continue to go mainstream, the next phase of the graph evolution has arrived. Cypher vs. Gremlin is no longer the right question to ask. The time has come to rethink graph analytics with TigerGraph and GSQL, the most complete query language on the market”
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“Above the Trend Line” – Your Industry Rumor Central for 5/29/2018
There is a lot of industry chatter around the need for a standard graph query language — and we at TigerGraph, couldn’t agree more. New technology is coming to the market to finally address decade-old issues around scale and performance — providing real innovation, rather than a repacking and renaming of the old. GSQL was built from the ground up to deliver features needed by real-time big data, including: ease of use, familiarity of SQL-like syntax, and expressive power for solving real-world complex business problems—something legacy language options can not offer.
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Crossing the Chasm — Eight Prerequisites For A Graph Query Language
There are many graph languages on the market, including Neo4j’s Cypher, Apache TinkerPop Gremlin and TigerGraph’s GSQL, to name a few. Before discussing which graph language is the best, or fusing the best aspects of each graph language into a new, unified option, let us take a step back to ask a more fundamental question: What are the prerequisites for a graph query language in the first place?
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Tools to Better Manage GDPR
TigerGraph offers the industry’s fastest graph analytics solution and supports GDPR with Real-Time Deep Link Analytics. Data will need to be stored and copied, and it will be need to be noted which applications are using it and for what purpose. TigerGraph can maintain a real-time map of all EU citizen data from the moment it is recorded and captured, where it is stored and copied, and detail its usage throughout the organization in hundreds or thousands of applications.
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Combating Fraud and Money Laundering with Graph Analytics
CEO Yu Xu explains how graph analytics has emerged at the forefront as an ideal technology to support Anti-Money Laundering (AML). Graphs overcome the challenge of uncovering the relationships in massive, complex and interconnect data. The graph model is designed from the ground up to treat relationships as first-class citizens.
Read the full article here.
Finding Needles in a Haystack With Graph Databases and Machine Learning
Graph features generated in real-time by TigerGraph are being used for a host of use cases beyond identifying phone-based scams. These include training Machine Learning to detect various other types of anomalous behavior, including credit card-related fraud — which affects all merchants selling products or services via eCommerce, and money laundering violations — spanning the entire financial services ecosystem and including banks, payment providers and newer cryptocurrencies such as Bitcoin and Ripple.
Read the full article here.
