Tables turning?

Graph databases see 250% spike in popularity

Lucy Carey
data.11

NoSQL is becoming more widely used across the board – but growing importance of data is fuelling interest in graph based schema.

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.

Fundamentally, Neo4j’s creator believes that the
ultimate purpose of data is to gain knowledge, and, to him, that’s
all about relating unknown concepts to known concepts. Any system
that does not easily facilitate this is ultimately useless.

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.

Any system that can facilitate this is ultimately
going to come out on top – and for graph DBMS, in 2014, their time
may have come at last.

Image by Elif
Ayiter

Author
Comments
comments powered by Disqus