The development of database technology is one of the defining achievements of the information technology era. It not only has been the key to dramatically improved record-keeping and business process ...

Graph databases have always been useful to help find connections across a vast data set, and it turns out that capability is quite handy in artificial intelligence and machine learning too. Today, ...

Vector databases are making a resurgence as generative AI hype sweeps across every industry. Organizations with developed graph and relational databases may question if they need to add another ...

Of the many NoSQL data management capabilities, the graph database offers special appeal to individuals who want to bridge the gaps between inherently connected information and apply graph analytics ...

Graph databases are the fastest growing category in all of data management, according to DB-Engines.com, a database consultancy. Since seeing early adoption by companies including Twitter, Facebook ...

Graph databases such as Neo4j, TigerGraph, Amazon Neptune, the graph portion of Azure Cosmos DB, and AnzoGraph, the subject of this review, offer a natural representation of data that is primarily ...

Graph databases explicitly express the connections between nodes, and are more efficient at the analysis of networks (computer, human, geographic, or otherwise) than relational databases. There has ...

Graph database vendor Neo4j Inc. is teaming up with Snowflake Inc. to make a library of Neo4j’s graph analytics functions available in the Snowflake cloud. The deal announced today allows users to ...

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TigerGraph Inc. is upgrading its graph database with a hybrid search capability designed to power artificial intelligence applications. The Redwood City, California-based startup debuted the feature ...