Digital/Electronic Information about Products, Customers, Orders and other aspects of business operations stored in the Databases in the form of sets of data so that the information is readily available and may be used in the transaction processing or business intelligence and analytics applications. As its term specifies, 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 that have been 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 has become the dominant technology for managing databases since then. Its hallmark is a row-based table structure that connects related data elements to one another and offers transactional veracity to maintain data accuracy and consistency; another common RDBMS element is support for using the Structured Query Language (SQL), a standardized programming language for writing queries to modify databases and retrieve data from them. The top RDBMS products in the market include Oracle Database, Microsoft SQL Server, IBM DB2 and MySQL, an open source database platform that is owned by Oracle; other notable ones are SAP HANA and PostgreSQL, another open source technology.
Let’s take a handier look at the difference between DBMS and RDBMS technologies, focusing on some general features of the former and specific ones that differentiate the latter from other types of DBMS software.
Introduction of DBMS
The overall DBMS world includes far more than relational database management systems. The first DBMS technologies to arise 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 connections to be mapped between data elements in different parent-child groupings. Examples of those types of systems 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 possessed by CA Inc. and marketed under the name CA IDMS with an added relational front-end framework.
Other DBMS categories that have been developed include tools for handling object-oriented databases that treat data as objects; columnar databases that are oriented more to columns than rows; and multidimensional databases that are designed precisely to support analytical querying by structuring data in a cube form defining various dimensions of the information at hand. However, all of those technologies were either swept aside or left with small market shares by the rise of the RDBMS, which began in earnest in the 1980s and has continued largely persistent through the ensuing decades.
In recent years, though, a divergent class of database systems grouped together as NoSQL technologies has found growing adoption primarily in big data applications, including ones involved with unstructured or semi-structured information. There are four main categories of NoSQL database management systems: key-value data stores, document databases, graph databases and wide column stores. One of the common attributes of NoSQL platforms is a flexible database schema, which enables them to accommodate data in different formats within a single database. The term NoSQL initially meant just that in many cases, but it has evolved to also stand for “not only SQL,” as vendors have incorporated elements of SQL into their products for programming uses.
The general concepts of database management are similar across the various DBMS categories. A DBMS sits between databases and the applications and end users needing to access the data stored in them, as well as database administrators (DBAs) looking to monitor and modify the databases they manage. Via queries and commands processed through the DBMS, end users can access, add and update data as part of business applications, while DBAs can track and tune the performance of database servers, change database structures, and manage database backup and recovery processes.
Introduction to RDBMS
E.F. Codd an IBM researcher defined first relational data model 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 started offering rival products. SQL, also initially developed at IBM and then adopted by Oracle and its rivals, became standardized in 1986 — although, individual vendors typically still offer versions of it with proprietary extensions. RDBMS sales took off as the client-server model of computing took hold in organizations, and relational systems began dominating the DBMS market by the mid-1990s.
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. That eliminates the need to redundantly store related data in multiple tables, which reduces data storage requirements, streamlines database maintenance and enables faster querying of databases.
Another notable difference between DBMS and RDBMS architectures, leaving the latter category out of the broad DBMS classification, 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 — for ensuring that database transactions are processed in a reliable way. That isn’t necessarily the case with other DBMS types — for example, many NoSQL databases guarantee a more limited form of ACID compliance, called eventual consistency.
While these RDBMS concepts and features provide reliable, stable and relatively robust processing of structured transaction data, relational technology does have some limitations — in particular, its requirement that databases include a rigid schema that’s difficult for DBAs to modify on the fly. That has helped create an opening for NoSQL software and, to a greater extent, file-based Hadoop clusters in big data environments, although relational databases are still at the center of most IT architectures.