Can Graph Integrate Data at Scale? Hint: Yes, But the Answer Isn’t What You Think.
‘The Enterprise Data Fabric’, a modern data management strategy, provides the ability to integrate data across the business rapidly while dealing with complexity and uncertainty by presenting data sets and data products using common business data models and addressing unanticipated questions and requirements.
Interoperability and How to Sustain It
Authored by Sam Chance, Principal Consultant at Cambridge Semantics, this article for Control Engineering discusses how semantic interoperability adds semantic annotations and knowledge enrichment to address issues with information not always being understood by recipients despite standards for data formats and structures.
The New Emerging Modern Data Infrastructure for Artificial Intelligence and Machine L …
Cambridge Semantics’ CTO, Sean Martin, writes in this article for Dataversity about how a Data Fabric architecture is the perfect data infrastructure for profiling, transforming, and preparing data for AI systems predicated on machine learning.
The Enterprise Data Fabric: an Information Architecture for Our Times
Our CTO and co-founder, Sean Martin, wrote this guest blogpost for Computer Weekly laying out our vision of what constitutes an Enterprise Data Fabric and the pivotal role graph technology and semantic standards play in its implementation.
Why Graph Databases Are the Right Choice for Many Data-centric Organizations
In this Medium article Steve Sarsfield, our VP of Product for AnzoGraph, discusses the ability of Graph Databases to store relationships and perform unique algorithms in a way that traditional Relational Databases just can’t, as well as several use cases where Graph Databases truly stand out.
Should a Graph Database Be in Your Next Data Warehouse Stack?
As interest grows in new technologies, many enterprises are reconsidering their data warehouses. In this article for TDWI, Steve Sarsfield, our VP of Product for AnzoGraph, discusses what makes graph databases different from traditional RDBMSs.