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.

Peter Neubauer introduces Graph databases and how they compare to RDBMS' and where they stand in the NOSQL-movement, followed by examples of using a graph database in Java with Neo4j.

Review: SQL Server 2017 adds Python, graph processing and runs on Linux Integration of Python for data science, graph processing for NoSQL-like functionality, and it runs on Linux as well as Windows.

Constructing these, however, is laborious and a very batch-oriented effort. Around the same time as scale-out NoSQL, graph databases emerged.

Although NoSQL has generated some noise of late, SQL continues to win in the marketplace and continues to earn investment and adoption throughout the big data problem space.

A NoSQL database has flexible data requirements, making it a better fit for applications that will evolve over time than an SQL database.

To SQL or NoSQL? That’s the database question But as technological lines blur, there's not always a clear-cut answer.

Reltio is a product that combines Master Data Management (MDM) with the power of the Cassandra NoSQL and graph databases to create a new kind of application development platform for data-heavy use ...

InfiniteGraph is the latest to throw an offering into the pot, launching a new tool for NoSQL quadrants of databases. It’s an enabling technology really.