One To Watch in the Big Data field

One To Watch: Aurelius’s distributed graph database, Titan

Chris Mayer
titan-logo

We always like to profile some exciting projects that come over the horizon making big noises – enter highly scalable graph database Titan

We always like to profile some exciting projects that come over
the horizon making big noises. One interesting project that has
recently been released by graph engineering speciallists Aurelius
is Titan
an open source, Apache2-licensed graph database
tailored for Big Data problem solving.

Having expertise in the world of Neo4j, TinkerPop and R
Statistics, Aurelius have offered their own alternative. The highly
scalable distributed graph database leverages existing
distributed storage engines such as Cassandra and HBase to
facilitate the representation and processing of massive-scale
graphs within a multi-machine cluster. A shrewd move, given how the
multi-cluster approach is getting increasingly important for many
Big Data developers. 

The single machine has outstayed its welcome – it can no longer
offer the storage and computational capacities that
larger data graphs need. Enter Titan – it supports an ‘infinite’
sized graph and an ‘unlimited’ number of concurrent
transactions through the addition of machines to the
cluster

In addition, Titan natively implements the TinkerPop
Blueprints API to ease the transition into existing architectures,
supports 
graph traversal language Gremlin and
integrates seamlessly with the Rexster graph server.

The presentation from Aurelius below explores the changing needs
of developers towards graph databases, and how Titan can change
things for the area. It’s encouraging to note the support for
already-established option Apache Cassandra and HBase, right from
the early days.

It’s a very impressive greeting from Titan – the team
behind the project clearly know their onions, and can visualise
them too! Titan looks ready to lift Big Data graphs to the pedestal
they belong on, focusing on concurrency, scalability and being
dynamic. Not to mention the inclusion of some important
standards.

The future looks well planned out too, with the inclusion
of Faunus, a path algebra for Hadoop, and Fulgora, an
in-memory graph engine. Now they just need to shout about it, to
gain adoption.

If you’re intrigued by this latest NoSQL/Big Data
contenders, check out the rather flashy GitHub home for
it. Another plus point is the excellent documentation – overflowing
with theory . That can only be a good thing. Keep an eye on this
one as it appears fully in July. The Graph Database market is
growing and we now welcome another to the fold.

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