Key characteristics

MongoDB has grown to a general purpose NoSQL database, offering the best of both RDBMS and NoSQL worlds. Some of the key characteristics are:

• It's a general purpose database. In contrast with other NoSQL databases that are built for purpose (for example, graph databases), MongoDB can serve heterogeneous loads and multiple purposes within an application.
• Flexible schema design. Document oriented approaches with non-defined attributes that can be modified on the fly is a key contrast between MongoDB and relational databases.
• It's built with high availability from the ground up. In our era of five nines in availability, this has to be a given. Coupled with automatic failover on detection of a server failure, this can help achieve high uptime.
• Feature rich. Offering the full range of SQL equivalent operators along with features such as MapReduce, aggregation framework, TTL/capped collections, and secondary indexing, MongoDB can fit many use cases, no matter how diverse the requirements are.
• Scalability and load balancing. It's built to scale, both vertically but most importantly horizontally. Using sharding, an architect can share load between different instances and achieve both read and write scalability. Data balancing happens automatically and transparently to the user by the shard balancer.
• Aggregation framework. Having an extract transform load framework built in the database means that a developer can perform most of the ETL logic before the data leaves the database, eliminating in many cases the need for complex data pipelines.
• Native replication. Data will get replicated across a replica set without complicated setup.
• Security features. Both authentication and authorization are taken into account so that an architect can secure her MongoDB instances.
• JSON (BSON, Binary JSON) objects for storing and transmitting documents. JSON is widely used across the web for frontend and API communication and as such it's easier when the database is using the same protocol.
• MapReduce. Even though the MapReduce engine isn't as advanced as it is in dedicated frameworks, it is nonetheless a great tool for building data pipelines.
• Querying and geospatial information in 2D and 3D. This may not be critical for many applications, but if it is for your use case then it's really convenient to be able to use the same database for geospatial calculations along with data storage.