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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.
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”
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.
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?
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.