Because Neo4j as a graph database management system provides many advantages when interacting with inherently networked and interconnected data structures, many traditional data analysis tools can benefit greatly from leveraging its query power. There is, most notably, an entire category of tools out there that are a prime candidate for doing so: the so-called Business Intelligence tools. Tools in this space include (but are certainly not limited to):

We will zoom into one of these tools in the following paragraphs, but would first like to clarify the mechanism through which these tools can interface with Neo4j. Of course, they could integrate by calling the raw Neo4j REST API and submittin specific requests this way, and would get responses in the rawest possible format. This type of integration would most likely require quite a bit of integration work, but could be advised in certain more advanced use cases.

There are, however, many use cases that can be solved by using a standardized integration mechanism that is overall well understood and readily available: most of the tools mentioned previously provide a mechanism for integrating with a standardized database interface called the Java Database Connectivity interface. This technology is an API for the Java programming language that defines how a client may access a database. It provides methods for querying and updating data in a database. By default, JDBC is oriented towards relational databases, but luckily for our discussion, a great deal of work has gone into a JDBC driver for Neo4j. You can download this driver from http://www.neo4j.org/develop/tools/jdbc and start experimenting with it very quickly.

Working with the Neo4j JDBC connector

As you can see from the preceding screenshot, you can write Neo4j queries in Cypher, send them over the wire via the JDBC driver to Neo4j, and receive the result set in a standardized tabular format. This format can then be used by tools like the Business Intelligence tools mentioned previously and integrated in their capabilities.

A very good case study of such an integration is provided by the way Qlikview integrates with Neo4j. Developed by a German company called TIQ Solutions (http://www.tiq-solutions.de/), it provides a full and comprehensive solution for integrating Neo4j with the Qlikview Business Intelligence suite of applications.

Integrating with Neo4j from Qlikview

All of these integrations make use of a JDBC connectivity that can be tuned to the Qlikview application development environment. For more information, you can visit http://www.tiq-solutions.de/display/enghome/ENJDBC and http://tiqview.tumblr.com/. The Neo4j website also features a number of links to examples and case studies that could be of use.

The mechanism outlined earlier, using the JDBC connectivity provided by Neo4j's driver, is a very powerful integration mechanism that can combine the best of the other worlds and can make the query power of graph databases such as Neo4j available to a broader user audience.