Graph databases facilitate discovery and analysis closely connected facts. This post is one of a series that introduces the fundamentals of NOSQL databases, and their role in Big Data Analytics.

Several NoSQL database categories have emerged, each tackling a distinct business problem: document, column array, key-value, and graphs. While the entire NoSQL sector is attracting increasing ...

If you want to know what’s what in Big Data analytics today, you’ve got to know the basics of NoSQL databases , and how appropriate NoSQL databases facilitate Big Data analytics.

Graph NoSQL databases support data models with connected data. In this article, author discusses security implications of graph databases in use cases like graph discovery and knowledge management.

NoSQL databases can take a number of forms. They can be cloud services or install on-premises. They can support one or more data models: key-value, document, column, graph, and sometimes even ...

Although a global graph database market estimated at $1.9 Billion in 2021 is expected to grow at 22.5% over the next five years, arguments continue on whether NoSQL graph databases are just ...

NoSQL databases are designed to store different types of data like Key Value, Documents, Time Series, Graph & IoT. Pascal Desmarets talks about how to do data modeling when using NoSQL databases.

At the high end of the complexity spectrum for NoSQL database lies the graph database, which are highly specialized data stores used for storing linked data. Instead of storing data in rows/columns or ...

Graph databases like Neo4J and ArrangoDB are mainly designed to store networks or interconnected nodes, but they often also use NoSQL’s simple model for the data stored at these nodes.

Around the same time as scale-out NoSQL, graph databases emerged. Many things are not “relational” per se, or not based on set theory and relational algebra, but instead on parent-child or ...