MongoDB Reveals Details Of Connector To SQL-Compliant Business Intelligence And Data Visualization Platforms

MongoDB today announced details of a technology that connects MongoDB to business intelligence and data visualization platforms such as Tableau, Business Objects, Cognos and Microsoft Excel. By rendering data stored in MongoDB compatible with SQL-compliant data analysis tools, the connector allows developers to leverage the rich querying ability of SQL to derive actionable business intelligence from MongoDB-based data. MongoDB customers can now directly take advantage of MongoDB’s connector to transform data from MongoDB’s JSON, nested format into the tabular format required of SQL-compliant tools, whereas previously, organizations interested in obtaining business intelligence on MongoDB-based data typically resorted to third party analytics and visualization platforms such as Jaspersoft, Pentaho and Informatica. By giving customers access to a richer, deeper connection between data aggregated in MongoDB and platforms such as Tableau and Business Objects, customers no longer need to consider transforming MongoDB-based data into a relational database prior to performing advanced analytical queries.

At this year’s MongoDB World conference, Tableau and MongoDB leveraged data from the U.S. Federal Aviation Administration to illustrate the likelihood that conference attendees would return home on time. The release of the connector is symptomatic of a broader, industry-wide trend toward deeper integration between NoSQL and SQL as evinced, for example, by the recent integration between Couchbase and Metanautix. Given the contemporary interest in real-time analytics on streaming Big Data, the obvious question raised by the tightened integration between MongoDB and SQL-compliant platforms concerns the degree to which BI platforms such as Tableau will be able to perform real-time queries on streaming data aggregated in MongoDB. Meanwhile, the release of the MongoDB connector illustrates the enduring popularity of SQL as a framework for querying heterogeneous datasets as exemplified by the way in which the convergence of SQL and NoSQL stands to complement the robust ecosystem of SQL on Hadoop platforms such as Lingual, Apache Hive, Pivotal HAWQ and Cloudera Impala.

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