At a high level, MongoDB enables developers that use data (which is most of us) to build easily, adapt quickly, and scale reliably. Let’s break that down:
MongoDB’s document model allows virtually any kind of data structure to be modeled and manipulated easily. MongoDB’s BSON data format, inspired by JSON, allows you to have objects in one collection have different sets of fields (say, a middle name on a user only when applicable, or region-specific information that only applies to some records).
MongoDB supports creating explicit schemas and validating data so it doesn’t get out of control, but this flexibility is an incredible asset when handling real-world data, and handling changes in requirements or environment.
If you’re used to having to bring down your site or application in order to change the structure of your data, you’re in luck: MongoDB is designed for change.
We spend a lot of time and effort designing efficient processes, and learning from our mistakes, but typically the database is slowing the whole thing down. There’s no downtime required to change schemas, and you can start writing new data to MongoDB at any time, without disrupting its operations.
What good is a database if you can’t find things inside it? MongoDB is designed to make data easy to access, and rarely to require joins or transactions, but when you need to do complex querying, it’s more than up to the task.
The MongoDB Query Language (MQL) is a full-featured, powerful language that allows you to query deep into documents, and even perform complex analytics pipelines with just a few lines of JSON-like MQL.
MongoDB is designed from the ground up to be a distributed database. Create clusters with real-time replication, and shard large or high-throughput collections across multiple clusters to sustain performance and scaler horizontally.