For TigerGraph Press Releases, click HERE.
Graph database startup TigerGraph Inc. is coming out with version 2.0 of its platform today, offering what it says is a unique feature that enables multiple users to work on the same graph simultaneously.
Graph databases are a type of NoSQL database that represent data as objects rather than in rows and columns. Connections can be traversed quickly to find relationships that would require multiple resource-consuming joins using relational technology.
TigerGraph, which provides a graph analytics platform for the enterprise, has announced TigerGraph 2.0 which introduces real time MultiGraph collaboration.
TigerGraph 2.0’s graph analytics collaboration service is significant, the company says, because it allows multiple groups to share one master database for access to real-time updates and collaboration. Local control and security features help enterprises meet compliance regulations.
Move over, doggy. 2018 is ‘the year of the graph’ apparently.
TigerGraph was founded in 2012 but only came out of stealth in September. Today it is launching its 2.0 platform, which it says offers more collaboration, and faster performance, than other graph databases.
Whether for Customer Analytics, Fraud Detection, Risk Assessment or another real-world challenge, the ability to quickly and efficiently explore, discover and predict complex relationships is a huge competitive differentiator for businesses today.
This elemental pain point – the need for real-time analytics for enterprises with enormous volumes of data – is fueling graph databases’ emergence as a mainstream technology being embraced by companies across a broad range of industries and sectors.
Where do IoT initiatives fail? A lack of talent and vision as well as a failure to identify the problems that projects are attempting to solve lead to failure.
Graph databases are gaining adherents for their performance querying related data. “The graph model offers an inherent indexed data structure, so it never needs to load or touch unrelated data for a given query,” Yu Xu, founder and CEO of graph database developer TigerGraph noted in a recent overview of graph technologies. “This makes it an excellent solution for better and faster real-time big data analytical queries.”
The year of the graph is here. Do you really need a graph database, and, if yes, how do you choose one? It’s official: graph databases are a thing. That’s the consensus here on Big on Data among fellow contributors Andrew Brust and Tony Baer. When AWS enters a domain, it officially signals the upward slope of the hype cycle. It’s a bit like newfound land – first it’s largely unknown and inhabited by natives, then the pioneers show there are opportunities, then the heavyweights will try to colonize it.
The movement to Enterprise Knowledge Graphs has been accelerated in the last months by two new developments. One is the addition of the Neptune graph database to Amazon’s database portfolio. The second is the funding of both cloud and on-premise graph systems like TigerGraph and other Bay Area startups. Many of the graph-architects from Google, LinkedIn and Facebook (all using graph databases) are now venturing out on their own to develop solutions for the enterprise.