(Related: NewSQL: Trying to solve what SQL and NoSQL can’t on their own) Hence the rise of another type of database, optimized for connected data: the graph database.

Graph database vs. relational database In a traditional relational or SQL database, the data is organized into tables.

(Image: DB engines) The gist of what's going on is that we have a war for graph database domination, and query language is a key battle to be won. SQL did not arrive at universal adoption overnight.

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 ...

Key Benefits of a Graph Database Better, Faster Queries and Analytics: Graph databases offer superior performance for querying related data, big or small. The graph model offers an inherent indexed ...

GSQL combines SQL-like query syntax with Cypher-like graph navigation, plus procedural programming and user-defined functions. I have mixed feelings about TigerGraph’s new GSQL query language.

The number one graph database, Neo4j, is kicking off its Graph Connect event today and announcing a new version, 3.3. This version brings extended support for querying in Spark, ETL, analytics ...

Cypher is the ‘SQL for graphs’ that has been missing in the Spark ecosystem, making the power of graph querying available to a much larger user base.” Neo4j is among a number of organizations that ...

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.

Startups like TigerGraph, MongoDB, Cambridge Semantics, DataStax, and others compete with Neo4j in a graph database market expected to be worth $2.4 billion by 2023, in addition to incumbents like ...