The most effective way to achieve this is to use the MongoDB Database-as-a-Service offering called MongoDB Atlas. MongoDB can handle dynamic data models and schema that can easily store unstructured data sets and in the case of variable data loads, MongoDB can scale horizontally, which is extremely cost-efficient when compared to traditional SQL databases, which can usually only scale vertically. Effectively, each MongoDB document follows the Javascript Object Notation (JSON) format. The data fields can be of an arbitrary number and type, even within the same collection. Indexes, a fundamental feature for efficient query execution, are also supported in MongoDB on any field or subfield of the documents, and they are defined at the collection level.Ī collection contains a number of documents, and a document contains data fields. In other words, in a relational database, tables are related by keys, and in MongoDB, collections contain unstructured and often arbitrary data.
In a traditional relational SQL database (i.e., MySQL, PostgreSQL), tables are used for defining the structure of a data set and rows for the actual data, while in MongoDB, we have collections and text documents containing name-value pairs, respectively. The best way to start understanding MongoDB basics is to grasp the following comparison: MongoDB