NoSQL databases, such as MongoDB and Cassandra, emerged in recent years to address the limitations of relational databases in meeting the requirements of the demands of modern applications.
This new generation of databases introduced many innovations, including flexible data models that accommodate the great variety of data types ingested by today’s Big Data applications. But they have little else in common except for the fact that they don’t use the relational data model.
The Cassandra data model, for example, is optimized for write performance, while sacrificing read performance and query functionality. Accordingly, these types of databases serve only a narrow set of applications.
A document database such as MongoDB, on the other hand, offers rich query functionality, and great performance for reads and writes. This allows MongoDB to serve a wide variety of operational and real-time analytics applications thanks to rich query functionality. The data model in MongoDB is:
- ** Document-oriented ** which means data is stored as documents that tend to have all data for a given record in a single document.
- ** Flexible ** as you are able to store any type of data along with sophisticated data access and rich indexing features.
- ** Dynamic ** since you can easily change the schema to accommodate changing requirements.
As a result, you expend less energy on setting up your data model in MongoDB and more time into developing the application. Download the white paper to learn more about the benefits of MongoDB’s flexible data model.