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Graph DBMS increased their popularity by 500% within the last 2 years
Graph DBMS are designed to model and explore relationships in data in a way not efficiently possible in other types of DBMS (including relational systems).
The demand for a graph data model often comes from use cases like analysis of social relationships, identity and access management, online product and service recommendations, network and device management (buzzword: Internet of Things) and financial fraud detection.
The analysts of Forrester Research recently reported that Graph DBMS will reach over 25 percent of all enterprises by 2017 (source: TechRadar: Enterprise DBMS, Q1 2014. Forrester Research.)
The three leading Graph DBMS in the recent DB-Engines ranking are:
Trend of DBMS categories
We have made a chart, that shows how the various DBMS categories have evolved since January 2013:
For this chart, we took the three most popular systems for each category, and tracked their popularity changes, normalized to 100 for January 2013. While RDBMS are quite stable, all other categories gained in popularity and Graph DBMS increased by more than 500 percent.
That said, we want to mention, that Relational DBMS are still much more popular than any other type of DBMS: they account for 82% of the total popularity score of all systems listed on DB-Engines and 7 out of the top 10 systems in the current ranking are relational systems.
Some other aspects underlining the Graph DBMS momentum
- Neo4J announced an additional $20 million funding by its investors in January 2015.
- Datastax acquired Aurelius LLC, the company behind TitanDB in February 2015, and plans to incorporate the graph data model into its enterprise database platform alongside Apache Cassandra and Solr.
- Some major vendors of relational systems already support the graph data model with their flagship products. E.g. Oracle and DB2 offer full support for RDF, which is a specific type of a graph data model with inference among relationships.