Why NoSQL?

Relational and NoSQL data models are very different. The relational model takes data and separates it into many interrelated tables that contain rows and columns. Tables reference each other through foreign keys that are stored in columns as well.

When looking up data, the desired information has to be collected from many tables (often hundreds in today’s enterprise applications) and combined before it can be provided to the application. Similarly, when writing data, the write needs to be coordinated and performed on many tables.

NoSQL databases have a very different model. For example, a document-oriented NoSQL database takes the data you want to store and aggregates it into documents using the JSON format.

Each JSON document can be thought of as an object used by your application. A JSON document might take all the data stored in a row that spans 20 tables of a relational database and aggregate it into a single document/object.

Aggregating this information may lead to duplication, but since storage is no longer cost prohibitive, the resulting data model’s flexibility, efficiency in distributing the resulting documents, and read and write performance improvement, make it an easy trade-off for web-based applications.

Figure 3. Document-oriented Database: A document-oriented NoSQL database stores and aggregates data in documents using the JSON format, instead of in tables of rows and columns as in the relational databases model. This approach results in better data model flexibility, greater efficiently in distributing documents, and superior read and write performance.

Another major difference is that relational technologies have rigid schemas while NoSQL models are schemaless.

Relational technology requires strict definition of a schema prior to storing any data into a database. Changing the schema once data is inserted is extremely disruptive and frequently avoided, which is a problem in the Big Data era when application developers need to constantly and rapidly incorporate new types of data to enrich their apps.

In comparison, document databases are schemaless, allowing you to freely add fields to JSON documents without having to first define changes. The format of the data being inserted can be changed at any time, without application disruption.