Database Reference
In-Depth Information
to express, Titan relies on the open-source graph traversal language Gremlin.
Determining the names of the companies that vertex 6's friends work for is ex-
pressed in Gremlin like this:
g.v(6).out('friend').out('worksFor').name
As graphs scale to the billions and trillions of edges, vertices in the graph can
accumulate millions of edges. Beyond its distributed nature, one of Titan's unique
advances in the graph space is vertex-centric indexes. In many ways, a vertex can
be seen as a table in the relational database sense (where each row is an edge).
When a table has millions of rows, an index is required to make row lookup ef-
ficient, else a linear scan of the table is required to locate rows that match the
provided predicate. Titan's vertex-centric queries, leveraging Cassandra's row in-
dexes and slice queries, allow for the efficient retrieval of a vertex's incident edges
according to an edge's label or properties. For example, what companies do vertex
6's best friends work for? The following traversal “jumps” to vertex 6's friends
and then to his best friends—no linear scan of all outgoing edges from vertex 6 is
required.
g.v(6).outE('friend')
.has('rating','best').inV
.out('worksFor').name
Cassandra delivers distributed data storage technology. Titan delivers graph
storage and processing technology. Together, these two technologies commingle
in support of an open-source, Apache2, distributed graph database.
Pentaho
Until 2012, organizations were not able to use existing business analytics products
with NoSQL databases such as Cassandra. The only way to get reports, visual-
izations, and analytics was via custom coding. This greatly limited the audience
who could tap into Cassandra's power and made it difficult and time-consuming
for those who could.
Pentaho changed that by offering the first Cassandra-based big-data analytics
solution for enterprises. This integration made it possible for developers, data sci-
entists, and business analysts to integrate and analyze both big-data and tradition-
