Chart-topper Neo4j makes connections in effort to nudge graph DBs into the mainstream

Java Database Connectivity built in to hook up easily with analytics tools

Graph database spinner Neo4j has built Java Database Connectivity (JDBC) into its tech as standard with the promise of making life easier for users of popular analytics and visualisation tools who want to work on graph data.…

Graph database spinner Neo4j has built Java Database Connectivity (JDBC) into its tech as standard with the promise of making life easier for users of popular analytics and visualisation tools who want to work on graph data.

The Neo4j BI Connector, effectively an API, is designed to present live graph datasets for analysis within business intelligence (BI) technologies such as Tableau, Looker, TIBCO Spotfire, Oracle Analytics Cloud, MicroStrategy and more, the company said.

David Allen, technology partner architect at Neo4j, told The Register that prior to the JDBC, analytics users might have had to take data out of the graph database in CSV files and add it into another database before loading it into their favourite tools.

“That requires more code and effort to do, and the data was always stale because whatever the BI folks are doing is going to be based on data that was exported at some previous time in the past,” he said.

Now users of Tableau, for example, can work on live graph data. “What we were looking to do is make it easier to tie graphs together with these BI tools,” Allen said. “A lot of times, when you’re doing a business intelligence investigation you’re not even looking at just one database, but you’re connecting data from across the enterprise.”

Hooking up

Although Neo4j has tested the connectivity tools with the listed vendors, it is not limited to these technologies. Analytics tools from IBM Cognos, Teradata Aster, and SAS also support JDBC, but Neo4j has not been able to test its connector with these as yet.

“A likely scenario is that, as we release future versions of the software and have the ability to test more packages with more customers, then without actually adding anything to the code, we’re just going to expand the list of what tools are supported,” Allen said.

But just because analytics tools can import data from graph databases does not necessarily make the output graph-like. Tableau’s strength lies in charts – bars, pies and lines – but not perhaps the tools necessary to visualise a network. Neo4J has its own network visualisation tool called Bloom.

The company is still working on that other popular way of connecting BI tools to databases, which is the Open Database Connectivity. “There are some enterprise tools that still require ODBC and we’re working on that and planning to release something in the future,” Allen said.

Michael Moore, national practice lead for enterprise knowledge graphs at consultancy firm EY, described the Neo4j BI Connector as “a huge step forward” to help Neo4j become a mainstream data management platform for graph-based insights. “Our team has been testing the Neo4j BI Connector with several popular business intelligence and data visualisation tools with great results,” he said.

Earlier this year The Register teased Neo4j about the niche status of graph databases compared to their relational cousins. If Moore’s testimony proves correct, new enterprise features like the Neo4j Connector could see that change. ®

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