Graph analysis will push big data evolution to the next plateau of scale and sophistication. Hadoop is one segment of the evolutionary picture, but it’s not necessarily the centerpiece.

Developers writing graph applications that scale into the billions of vertices and trillions and edges are forced to make trade-offs that impact the performance of the application. But thanks to new ...

Natural language processing techniques are used to extract entities and relationships from data, such as text documents, and populate the graph with information from various sources is essential.

A new open-source library by Nvidia could be the secret ingredient to advancing analytics and making graph databases faster. The key: parallel processing on Nvidia GPUs.

The prevalence of big data and the value it generates has greatly, and perhaps indelibly, altered contemporary business practices in two pivotal ways. Firstly, the value of just-in-time analytics is ...

Similarly, Spanner’s graph processing capabilities, according to Nucleus Research senior analyst Alexander Wurm will allow Google to compete with the likes of Neo4j, Amazon Neptune, and ...

Graph data meets GIS maps. Connecting graph representations of data with geographic data has particular value in many applications, according to Jean Villedieu, cofounder and managing director ...

While privacy advocates have expressed concern about the phenomenon of massive data collection and analytics colloquially known as “big data,” most people are more familiar with social media ...

Apache Spark: Best for open-source big data processing. ... Supports SQL analytics, streaming data, machine learning and graph processing. Data science on petabyte-scale data.