Data processing speed
RDBMSs excel at looking up multiple items of data associated with tables and at transaction processing. These require maintaining database integrity and consistency, which can cause overhead and slow data processing speeds when working with a large amount of data. NoSQL DBMS, on the other hand, has a simple structure. There is no need to look up multiple items of data or maintain consistency across tables, so processing can be performed quickly.
Data distribution and scalability
RDBMSs are designed to operate on a single server in order to maintain data consistency, so it is difficult to distribute data across multiple servers. In order to deal with the growing amount of data, servers must therefore be scaled up by replacing their hardware with higher performance hardware. On the other hand, NoSQL DBMSs place limits on the scopes of data consistency so that data can be horizontally distributed. Systems can be expanded simply by adding servers, which provides major cost benefits.
RDBMSs provide strong support for atomicity, consistency, isolation, and durability (ACID). If, for example, an error occurs during a transaction, the processing results will not be reflected in the database, preventing data contradictions and inconsistencies. NoSQL DBMSs, on the other hand, use a data integrity model that is based on the CAP theorem. The CAP theorem states that any given decentralized system can only achieve two of the following three: consistency, availability, and partition tolerance. In other words, NoSQL DBMSs select two whether to maintain consistency, availability, or partition tolerance in a decentralized system. While RDBMSs are focused on transaction reliability and thus maintain data consistency, NoSQL DBMSs are focused on flexibility and scalability, so data consistency may not be assured.
RDBMSs have the advantage of being able to store, query, and update data using SQL, a standard query language that has long been supported by many application developers. NoSQL DBMSs, on the other hand, have unique application programming interfaces (APIs) and provide query languages appropriate for specific applications and data models.
RDBMSs are database management systems suited for use with traditional operation systems, which require structured data, transaction processing, and data consistency. NoSQL DBMSs, in contrast, are suited to the management of big data such as IoT data, which requires fast, scalable processing.