SQL to MongoDB is a powerful advantage as the world of Big Data and NoSQL meets the enterprise where SQL is the lingua franca.  Jack Vaughan at SearchDataManagement published a recent post entitled “Marketing company applies SQL to MongoDB to uncover social media trends“.  In his article, Vaughan talks about SumAll – a marketing analytics firm.  SumAll uses SQL to MongoDB to “create analytical dashboards that let customers estimate the return on investment and compare results for marketing campaigns across social media platforms.”  While MongoDB supports JSON well and is scalable, the architecture of analytical tools today still revolves around the SQL query language.  Therefore, being able to query your MongoDB database using the SQL query language is a powerful differentiator in making your data truly accessible and getting the most value from it.  Vaughan writes that “SumAll tracks 100s of billions of social impressions over billions of rows” and ” it can be difficult to analyze and aggregate MongoDB data.”

The article by Jack Vaughan shows but one example of the power of SQL to MongoDB. In fact, more generically, the power to query any data source using the SQL query language opens up the universe of BI and analytics tools that have been developed over the last 30 or more years.  Being able to connect tools like Excel, Tableau, Crystal Reports, etc to MongoDB and do the reporting and analytics you need today, gives you a serious advantage against your competitors.  ODBC and JDBC drivers have been and continue to be the tools used to connect applications to data.  Including SQL to MongoDB right within the ODBC driver takes the complexity out of accessing and analyzing your data.  If you need help on SQL to MongoDB, I strongly suggest reading Kyle Porter’s great Quick Start tutorial on using the MongoDB ODBC driver.