In today’s data-driven world, enterprise owners need to make the most of their data by organizing it for effortless accessibility. That’s precisely where a MongoDB database comes in. It is a document-oriented NoSQL database used to store high-volume data. It defies the traditional methods of utilizing tables and rows and instead leverages collections and documents. That’s precisely the purpose and definition of NoSQL Database in a terse way. In MongoDB Database, documents are the basic units of data. These documents are quite similar to JavaScript Object Notation but adopt a variant called Binary JSON (BSON). This enables users to accommodate more data types. The values included can be of a variety of data types, including other documents, arrays, and arrays of documents. In MongoDB Database, sets of documents are called collections. These collections function as the equivalent of relational database tables. Collections can contain any type of data, however, collections can not be spread across different databases. Initially, NoSQL Database climbed a wave of popularity to a peak of Puffed Up Expectation, then gradually fell to the Trench of Disillusionment. NoSQL Databases are now standing with its head held high on the Highland of Productivity and Efficiency. These databases are flexible enough to adapt themselves to the comfort zones of enterprise owners and as per the use cases. NoSQL Databases like MongoDB are a great choice when data is document-centric and does not fit well into the schema of a relational database when one needs to accommodate a massive scale of data when one is rapid prototyping and a few other cases which will be mentioned comprehensively in this article. But first, let’s zoom in a bit more into MongoDB Database.