One of the most important reasons for the popularity of MongoDB is that it is a JSON-friendly database. This means that documents are stored and retrieved from MongoDB as JavaScript objects. Internally, this JSON data gets converted to BSON format while getting persisted to the system. So, this gives extreme flexibility, where we can use the same data format from client to server and eventually to the database.

A typical document (record) in a MongoDB collection (table) might look like the following code:

$ mongo 
> db.contacts.find({email: '[email protected]'}).pretty() 
{ 
   "email" : "[email protected]", 
   "phone" : "123-456-7890", 
   "gravatar" : "751e957d48e31841ff15d8fa0f1b0acf", 
   "_id" : ObjectId("52fad824392f58ac2452c992"), 
   "name" : { 
      "first" : "Jason", 
      "last" : "Krol" 
   }, 
   "__v" : 0 
} 

After examining the preceding output, we can see a key called _id. It is a MongoDB ID which must be encoded as a binary JSON objectID(BSON). If failed to encode, MongoDB won't be able to retrieve or update an object.

Another important feature of MongoDB is its schema-less nature. With a relational database, you are required to define the exact structure of the data being stored ahead of time, which is termed as the schema. This means that you must have defined the exact number of columns, the length, and the data type for every field in a table, and that each field must always match that exact set of criteria. Mongo provides a flexible nature where the documents that you store into the database need not follow any schema unless the developer enforces it through the application level. This makes MongoDB a great fit for Agile-based development, as you could carry out modifications on the application schema on the fly.

Other than the JavaScript-friendly nature, one other resemblance between MongoDB and Node.js is that MongoDB is also designed with highly concurrent applications with heavy read operations in mind.

MongoDB also introduces the concept of sharding, which makes it possible to scale the database horizontally as well as vertically. If the application owner needs to increase the database capabilities, they could add more machines into the stack. This is a cheaper option compared to investing in the RAM of a single machine, which will be the case in RDBMS solutions.

The process of Indexing created a list of values called index for a chosen field. These indexes are used to query larger chunks of data. Using indexes accelerates the data retrieval speed and performance. The MongoDB client provides various methods like ensureIndex to create an index only if one doesn't exist.

Additionally, MongoDB has various commands to allow aggregation of data, such as group, count, and return distinct values.

All the advantages that we discussed come with some impact on the consistency, as MongoDB does not strictly adhere to the RDBMS standards like ACID transactions. Also, if you end up creating a data model that might need too many JOIN operations, then MongoDB won't make a good fit as it is not designed with too many aggregations, even though the aggregations are possible via the MongoDB aggregation framework. MongoDB may or may not be the right solution for your application. You should truly weigh the pros and cons of each technology before making a decision to determine which technology is right for you.