Rising minority

Graph database growth curve spurred on with release of Neo4j 2.1

Lucy Carey
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Latest Neo Technology release comes packed with built-in ETL, making data import from relational databases and other data sources easier.

Graph database pioneers Neo Technology shook up the traditional
RDMS world in 2000 with the launch of their radically different
data management system. Following a decade or so of relatively
conservative tinkering, the shock-meisters were at it again in
December, with a shake-up
of their core data model in Neo4j 2.0 (full summary of all
the ace new additions

here)
Although their release of Neo4j
2.1 this week lacks any similarly dramatic changes, it has had a
good number of tunings geared at upping productivity and
performance.

With this latest drop, Neo4j now has an
integrated ETL process (which stands for “Extract, Transform,
Load”), which enables seamless data importing from relational and
other data sources. The process makes it possible to combine data
from differently structured data sources into a single target
database.

Neo Technologies co-founder Emil Eifrem

commented
that: “Neo4j 2.1 represents a step
forward in lowering the bar to graph database adoption for
organizations who have massive amounts of data in their relational
databases,” adding that “Companies are recognizing the value that
comes from reimagining their existing data as a graph. The new
built-in ETL capabilities here enable when moving data from an
RDBMS into a graph.”

There were also alterations to Cypher query
language within Neo4j 2.1 (
here’s
a handy tutorial if you’d like to find out more about
Cypher). As the image below demonstrates, it now supports the
extraction and mapping from CSV files.

With its unique design, Neo4j occupies a special
place in a highly competitive NoSQL market, packed to the rafters
with diverse data solutions. Because of its quixotic model, the
open source tech has no other NoSQL competitors – and whilst it’s
certainly not the perfect solution for everyone,  the inherent
ability of graph DBMSs to represent and process a multitude of
different objects and the many connections between means there are
a host of potential use cases for the technology.

As Eifrem observes, “the majority of the world
still doesn’t know that graph databases exist,” although with
around 30 – 40 groups working on projects, products and companies
related to graph databases, including Oracle IBM, SAP, and
Facebook, that’s rapidly changing.

A (slightly contentious) study by

DB-ENGINE
concluded that, in 2013, graph
databases enjoyed a 250% boost in popularity – something that may
well be partly attributable to the maturation of the technology.
For example, with Neo4j 2.0, users can now create ‘subgraphs’
within their datasets, giving a leaner, simpler, and guaranteed
indexing mechanism to the data. As it’s evolved, the technology has
also become far easier to use, making for less intimidating novice
user experiences.

Whilst there’s been some debate on DB-ENGINE’s
methodology, there’s no denying that, after a decade of relative
obscurity, the world is opening up to the power of graph DBMS, with
Forrester estimating, by 2017, over 25 percent of enterprises will
be employing the technology in some fashion.

If you’d like to get ahead of the curve (we couldn’t
resist), have a watch of this talk from JAX London 2013,
 where Ian Robinson presents some design and implementation
strategies you can employ when building a Neo4j-based graph
database solution:

neoo

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