Graph + AI World - Enterprise Architecture

How Graph + AI Fits in Your Enterprise Architecture

Graph + AI World Session - Recorded September 2020

Today's data architectures are characterized by cloud computing, vast volumes of data, a range of computational and processing frameworks and change. In this brief overview, we give a bird's eye view of TigerGraph from the perspective of the system architect and highlight the key ways in which TigerGraph's scalable deep-link graph analytics can add value and performance to your ML/AI architecture.

About TigerGraph

TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph’s proven technology supports applications such as fraud detection, customer 360, MDM, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Amgen, China Mobile, Intuit, Wish and Zillow, along with some of the world’s largest healthcare, entertainment and financial institutions.

Graph + AI World:

How Graph + AI Fits in Your Enterprise Architecture

Graph + AI World Session - Recorded September 2020

Watch the Session

You gotta use the right tool for the right job. If you only have one user in the system, you are just doing single analytics and you have time to load terabytes of data into your RAM, Spark might be an option. But it is only going to fit about 2-3% of the use cases that we use TigerGraph for.

Dan McCreary, Optum

About TigerGraph