Graph-Powered Analytics: Why You Need It and How to Learn It

Graph data and graph analytics are becoming increasingly important in businesses as they help to improve analytics, cut losses, and find more revenue. Graph databases allow for faster and more accurate analysis of interdependencies and interrelations compared to other types of databases. Key use cases include financial crime detection, customer 360 with entity resolution, personalized recommendations, and modeling and optimization of operational systems and networks. The book "Graph-Powered Analytics and Machine Learning with TigerGraph" aims to address the needs of readers with varying technical backgrounds and learning styles by presenting material in concept chapters and hands-on use case example chapters. It also demonstrates using TigerGraph, a scalable and fast platform for graph analytics, along with its GSQL query language and GraphStudio Starter Kits.