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When selecting a NoSQL database, MongoDB is often one of the top choices. Unlike traditional SQL databases, NoSQL databases are known for their ability to work with large datasets, which offers scalability and flexibility in app development. SQL databases, on the contrary, are comparatively stiff and not easily scalable when dealing with large sets of data
For a pretty decent amount of time, doing analytics with MongoDB required additional overhead compared to modern SQL RDBMS and Data Warehouses associated with aggregation pipeline and MapReduce practices. While this approach allowed you to craft advanced custom tailored aggregation algorithms, it required additional knowledge of how to build and maintain it.
To fill this gap, MongoDB released the MongoDB connector for BI, which acts as a MySQL server on top of your MongoDB data. Under the hood, it bridges existing aggregation mechanisms to the MySQL protocol, allowing standard MySQL clients to connect and issue SQL queries.