Evaluating the Leading Graph Databases – TigerGraph and Neo4j for Scalability
As the decade of Graph roars on, it’s important to evaluate each available offering based on objective criteria. I mentioned in the last blog that architecture is an important consideration…
The Decade of the Graph: 2021 Illustrates that Graph is entering the mainstream
TigerGraph came out of stealth in 2017, and every year since has been coined “The Year of the Graph” by experts, journalists, and market watchers due to the accelerating momentum. …
Jaguar Land Rover Increases Supply Chain Planning Velocity with Graph — And Wins CIO 100 Award for Impressive Results
By Yu Xu, TigerGraph CEO & FounderI am very pleased to extend my congratulations to Harry Powell, Director of Data and Analytics at Jaguar Land Rover, and his team for…
Analytical Transaction Processing (ATP): Real-Time, Graph Decision-Making Made Easy
Originally posted on ITProPortal There are two “must have” ingredients to making effective business decisions: a complete contextual view of the situation and real-time data. Carl Olofson, Research Vice President…
Five Data Analytics Predictions for 2020
The phrase “2020 vision” implies that someone is able to see clearly. In this article I’m going to examine five technologies that have the potential to have a profound impact…
Machine Learning Applied to Detecting Fraud in Healthcare
Fraud is a major contributing factor to escalating healthcare costs: fraud costs the U.S. about $60 billion annually and accounts for up to 10 percent of total healthcare spending. Is…
Just a Key-Value Problem? How Graph Reduces Memory Consumption, Accelerates Performance and Overcomes Problems with Key-Value Databases
Key-value databases have long been used for a variety of applications to provide pre-computed results in real-time. There is, however, an increasing shift towards using Graph technology for real-time applications…