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

• It is a general purpose database: In contrast to other NoSQL databases that are built for specific purposes (for example, graph databases), MongoDB can serve heterogeneous loads and multiple purposes within an application. This became even more true after version 4.0 introduced multi-document ACID transactions, further expanding the use cases in which it can be effectively used.
• 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 is 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 upon detection of a server failure, this can help to achieve high uptime.
• Feature rich: Offering the full range of SQL equivalent operators, along with features such as MapReduce, aggregation framework, Time to Live and capped collections, and secondary indexing, MongoDB can fit many use cases, no matter how diverse the requirements are.
• Scalability and load balancing: It is built to scale, both vertically and (mainly) horizontally. Using sharding, an architect can share a load between different instances and achieve both read and write scalability. Data balancing happens automatically (and transparently to the user) via the shard balancer.
• Aggregation framework: Having an ETL 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 their MongoDB instances.
• JSON (BSON and Binary JSON) objects for storing and transmitting documents: JSON is widely used across the web for frontend and API communication, and, as such, it is easier when the database is using the same protocol.
• MapReduce: Even though the MapReduce engine is not 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 is really convenient to be able to use the same database for geospatial calculations and data storage.
• Multi-document ACID transactions: Starting from version 4.0, MongoDB supports ACID transactions across multiple documents.
• Mature tooling: The tooling for MongoDB has evolved to support from DBaaS to Sync, Mobile, and serverless (Stitch).