By Aileen Agricola | March 3, 2015
Graph DBMS Increased by 50% within the Last 2 Years
Graph DBMS are designed to model and explore relationships in data in a way not efficiently possible in other types of DBMS (including relational systems).
The demand for a graph data model often comes from use cases like analysis of social relationships, identity and access management, online product and service recommendations, network and device management (buzzword: Internet of Things) and financial fraud detection.
The analysts of Forrester Research recently reported that Graph DBMS will reach over 25 percent of all enterprises by 2017 (source: TechRadar: Enterprise DBMS, Q1 2014. Forrester Research.)
The three leading Graph DBMS in the recent DB-Engines ranking are:
This book provides an insight into working with Neo4j; deployment, configuration, and optimization of the data models; and utilizing storage for better performance. Also it covers all aspects related to working with Neo4j, including querying, indexing, modeling of graph data, testing, and deployment of your Neo4j applications, and also shows you the internal features of the Neo4j graph database. With a sample demonstration and outline of community developed tools, this book will help you develop cutting-edge, high performance, and secure applications for complex data using the Neo4j graph database.
“Analyzing BitCoin Network Transactions with Neo4j” by Michael Hunger
Besides helping our customers to be successful trying and using Neo4j, DC-based David Fauth is always on the lookout for interesting new datasets to analyze.Being a (big) data scientist and data analyst by heart, he excels in deriving new insights from existing data and explaining the intricate connections that reveal it.In previous installments he analyzed the FEC Campaign Data with Neo4j and showed how to import DocGraph Datasets.