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.
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.
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.
TigerGraph is providing the next evolutionary step in Graph Databases. It is the first system capable of performing Real-Time Analytics of data on a web-scale. The Native Parallel Graph (NPG) is designed to focus on both computation and storage, while supporting graph updates in real-time and providing built-in parallel computations. An SQL-like graph query language GSQL allows ad-hoc exploration, and supports the analysis of Big Data. With expressive capabilities and NPG speeds, users can perform Deep Link Analytics to uncover connections and insights previously inaccessible.
TigerGraph’s Product Manager, Victor Lee, describes how TigerGraph achieves fast data ingest, fast graph traversal, and deep link analytics even for large data sets
The ability to draw deep connections between data entities in real time requires new technology designed for scale and performance. There are many design decisions which work cooperatively to achieve TigerGraph’s breakthrough speed and scalability. Below we will look at these design features and discuss how they work together.
Real-time big graphs represent the next stage in the graph database evolution, and are designed to deal with massive data volumes and data creation rates to provide real-time analytics.
Enterprises demand real-time graph analytic capabilities that can explore, discover and predict very complex relationships. This represents deep link analytics, achieved utilizing three to 10+ hops of traversal across a big graph, along with fast graph traversal speed and data updates.
Technologies leveraging graph databases will power more and more enterprise AI, machine learning, cyber security and IoT applications. And the graph space continues to grow – take for example, TigerGraph’s emergence in September with the industry’s first Native Parallel Graph technology, and Amazon’s recent announced of a limited preview of its Amazon Neptune graph cloud offering.
Dr. Yu Xu: Today, companies are demanding real-time data to make informed decisions and to provide better customer experiences. Graph analytics are optimized to deliver new insight and intelligence previously impossible or hard to detect, allowing enterprises to capture key business moments for competitive advantage.
When data are modeled and represented in a graph structure, as nodes and the set of connections (edges) between those nodes, new perspectives are revealed. Graph analytics leverages connections between data to better reveal patterns, non-obvious relationships, correlations and sequences.
If the history of relational databases is any indication, what is going on in graph databases right now may be history in the making.
TigerGraph on its part recently announced version 2.0, which it says comes with performance improvements. But what is maybe most noteworthy is what TigerGraph says is a unique feature that enables multiple users to work on the same graph simultaneously.
This is called MultiGraph and is meant to give organizations control over which parts of a graph or graphs users can access while maintaining security and data integrity.
Last year, Baidu bought Kitt.ai in Seattle, and invested some an estimated $40 million in three machine learning and data companies: Silicon Valley-based data link analytics entrant TigerGraph, big data application Tiger Computing Solutions, and computer vision startup xPerception.