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.

IBM releases Graph, a service that can outperform SQL databases by Dan Richman on July 27, 2016 at 3:25 pm Share 1 Tweet Share Reddit Email ...

For example, TigerGraph recently used these benchmarks to scale its database to support 30 terabytes (TB) of graph data, up from 1 TB in 2019 and 5 TB in 2020.

You can think of a graph database as a set of interconnected circles (nodes) and each node represents a person, a product, a place or ‘thing’ that we want to build into our data universe.

Facebook reaches Social Graph database benchmark: Facebook engineers discuss the development processes behind LinkBench, and how it’s being used at the world's largest social network today.

But SQL’s hold on data retrieval is slipping. New databases are emerging, and some speak entirely new languages. It’s not that SQL is becoming less popular.

There are a number of graph databases available which are mostly tailored especially to meet one or several use-cases and as always it's great for customers to have choice.

They’re part of Neo4j’s broader goal to take graph databases beyond queries of raw data to predict outcomes based on connections. Specifically, the company is adding three new embedding options.

The graph database is experiencing a surge in popularity, thanks to all of the above. This type of database, which uses graph structures for semantic queries with nodes, edges and properties to ...