As he notes, “It’s ironic that our dominant database system is poor at handling connections, because that’s what turns data into knowledge.”The ways in which businesses interact with potential customers continues to increase, and in tandem than that, so too does the volume of data which they have at hand to make connections and find competitive sweet spots.
While only two NoSQL DBMS made it into DB-ENGINE’s ranking of databases in terms of popularity in 2013 (New York startup du jour MongoDB and Cassandra), it was graph databases that really seem to have enjoyed a dramatic ascent in the past year, with a 250% boost in popularity.
MongoDB remains by far the biggest NoSQL player, ranked at number six, followed by Cassandra at 10 and Redis at 13. With MongoDB having received a record breaking cash injection in the fall of 2013, this status quo is likely to remain in place for some time.Whilst Graph DBMS pioneer Neo4j jumps one place up from its 2012 ranking to 23 on the table, ultimately, it scrapes less than 10% of the points accrued by the seemingly indomitable NoSQL leader.
For the moment, traditional relational databases still continue to outpace any other schema, with RDBMSs occupying eight out of ten of the highest ranked models by DB-ENGINES. Currently, the biggest NoSQL provider, New York mega startup MongoDB has less than one eighth of Oracle’s total score.
Relational databases still have an important role to play in data storage, and, in some use cases, are still the best option. For this reason, RDBMS rankings remained pretty steady over the past 12 months, and will likely continue to do so for some time.
What these figures do demonstrate is a quite dramatic shift over the past months regarding how people are perceiving NoSQL DBMSs. In the case of graph database technology, whilst it has been around for a decade now, it’s only in the past 12 months that interest has really picked up around it.
A large part of this could be down to the maturation of the technology. December saw the release of a “quite extraordinary” reimagining of graph DBMS pioneer Neo4j – with the data model, which had previously gone unchanged for a decade, recalibrated for the first time. With Neo4j 2.0, users can now create ‘subgraphs’ within their datasets, giving a leaner, simpler, and guaranteed indexing mechanism to the data.
It’s changes such as these that are helping to attract new users. As it’s grown up, the technology has also become far easier to use, making for less intimidating novice user experiences.
Additionally, the inherent ability of graph DBMSs to represent and process a multitude of different objects and the many connections between them opens up a host of potential use cases for the technology – and with companies such as Hewlett Packard starting to pay attention, we can expect to see more and more use cases in the months to come.
Keywords: DB Engines