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Databases store sets of data about products, customers, orders and other facets of business operations for use in transaction processing or BI and analytics applications. A database management system is software that supports the development, administration and use of databases. DBMS is an umbrella term for different types of database management technologies developed over the past 50-plus years.
A relational database management system (RDBMS) is a kind of DBMS that was created in the 1970s and eventually became the dominant database technology. Its hallmarks are a row-based table structure that connects related data elements to one another and support for the Structured Query Language (SQL), a programming language used to manage relational databases and retrieve data from them.
Let's take a closer look at the difference between DBMS and RDBMS technologies, focusing on some general characteristics of the former and specific attributes that differentiate the latter from other types of DBMS software.
What is a DBMS?
The DBMS universe includes far more than RDBMSes. The first DBMS technologies to emerge in the 1960s supported hierarchical databases, in which data is organized in a treelike structure with parent and child records, and network databases, which enabled relationships to be mapped between data elements in different parent-child groupings.
Such products that are still available include IBM's Information Management System, better known by its acronym IMS, and the Integrated Data Management System, a product now owned by CA Technologies and marketed under the name CA IDMS with an added relational front-end framework.
Other DBMS categories include systems for managing object-oriented databases that treat data as objects; columnar databases that are oriented more to columns than rows; and multidimensional databases that are designed specifically to support analytical querying by structuring data in a cube form, delineating various dimensions of the data.
In recent years, a diverse set of DBMS technologies grouped together as NoSQL systems has found growing adoption, primarily in big data applications that involve unstructured or semistructured data. There are four main classes of NoSQL software, united by support for flexible database schemas: key-value stores, document databases, graph databases and wide column stores. Prominent NoSQL DBMS products include MongoDB, Cassandra, Amazon DynamoDB, MarkLogic, Neo4j, Redis and HBase.
The general concepts of database management are similar across the various DBMS categories, including RDBMS platforms. A DBMS sits between databases and the applications and end users connected to them, as well as database administrators (DBAs) who monitor and manage databases. Via queries and commands processed in the DBMS, end users can access, add and update data, while DBAs can tune performance, change database structures and manage backup and recovery processes.
What is an RDBMS?
Relational software uses the concept of database normalization and the constraints of primary and foreign keys to establish relationships between rows of data in different database tables. Primary keys are unique identifiers for a table's rows -- for example, a customer ID number could be the primary key in a table with data on a company's customers. Foreign keys point to primary keys in other tables.
The ability to link related data values via the use of keys eliminates the need to store data in multiple tables. That reduces data redundancy, which, in turn, lowers storage requirements, streamlines database maintenance and enables faster querying in RDBMS environments.
The relational model was first defined by IBM researcher E.F. Codd in a technical paper published in 1970. Oracle released the first commercial RDBMS in 1979, when the company was called Relational Software Inc., and other vendors soon developed rival products. SQL, also created at IBM, was adopted as a common programming language for relational databases and then standardized in 1986 -- although RDBMS vendors still offer versions of it with proprietary extensions for querying and data manipulation.
The rise of the RDBMS began in earnest in the 1980s as the client-server model of computing took hold in organizations, and relational systems began to dominate the DBMS market by the mid-1990s. The top RDBMS products now include Oracle Database, Microsoft SQL Server, IBM Db2 and MySQL, an open source database platform owned by Oracle; other notable ones are SAP HANA and PostgreSQL, another open source technology.
How do RDBMS and other DBMS platforms differ?
The row-based table structure in relational databases is a key difference between DBMS and RDBMS architectures, leaving the latter category out of the broad DBMS classification. Other types of DBMSes don't store data in the same kind of tabular form or follow the data modeling concepts that are built into the relational approach.
Another notable difference is relational technology's support for referential integrity and other integrity checks designed to help keep data accurate and prevent inconsistent information from being entered in database tables. That's part of an adherence to the ACID properties -- atomicity, consistency, isolation and durability -- which ensure that database transactions are processed in a reliable way.
Other types of DBMS software don't necessarily provide the same level of transactional integrity. For example, many NoSQL databases guarantee a more limited form of ACID compliance, called eventual consistency. That isn't universal, though. Some NoSQL vendors have now built stronger ACID support into their software.
While RDBMS concepts and features support reliable, stable and relatively robust processing of structured transaction data, relational technology does have some limitations of its own -- in particular, its requirement that databases include a rigid schema that's difficult for DBAs to modify on the fly.
By comparison, the flexible database schemas that are a common attribute of NoSQL platforms enable them to accommodate data in different formats within a single database. That flexibility has helped create an opening for NoSQL software in big data environments, although relational databases are still at the center of most IT architectures.
The difference isn't absolute, though. For example, CQL has similarities to SQL. And while the term NoSQL initially meant just that, in many cases, it has evolved to also stand for "not only SQL," as NoSQL vendors have incorporated elements of SQL into their products for programming uses.
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