Relational database definition

A relational database is a collection of data organized into a table structure. This concept, proposed by IBM mathematician Edgar F. Cobb in 1970, revolutionized the world of databases by making data more easily accessible by many more users. Before the establishment of relational databases, only users with advanced programming skills could retrieve or query their data.

Within the table structure, the rows are called “records” or “tuples” and the columns are called “attributes.” The structure allows users to identify and access data in relation to another piece of data in the table, or other tables within the database. Tables can be modified, or rows and columns can be added or removed without affecting the rest of the database.

What is an RDBMS?

An RDBMS, or relational database management system, is the software that gives users the ability to update, query and administer a relational database. Structured Query Language (SQL) is typically the standard programming language used to access the database. To offer more flexibility, the SQL standard has been modified to enable storage, retrieval and publishing of JSON data within a relational database. Modern database management addresses the need for integrating, managing and analyzing data from multiple sources across on-premises and cloud environments. For example, the addition of the object relational model, which is similar to a relational database, has enabled vendors to offer extensions that support data types that are not part of the SQL standard, such as time-series data.

What is a containerized database?

Today’s application developers are taking advantage of containers and microservices to free them from the constraints of a traditional monolithic approach. Containers are small and lightweight, making them ideal for a microservices architecture where applications are packaged together with dependencies and libraries, while running services on isolated environments. These services can include the database functions and operate on the same codebase as the governance and data science services. All can be deployed within containers virtually anywhere the underlying platform can be, including hybrid and multicloud environments. Also, running multiple containers on the same infrastructure saves time and money.

Why care about relational databases?

The world runs on them

The relational database and relational DBMS have been at the core of most mission-critical business and government transactions for decades. Looking ahead, they will continue to evolve in their capabilities and be a critical component for services leveraging modern technologies, such as AI, cognitive, big data, predictive analytics and more. While graphs, cubes, tensors and MapReduce are capturing much of today’s mindshare, tomorrow’s hottest applications will still be built on the back of tried and true SQL.  

As enterprise architects and data scientists embrace newer data architectures, they will want to retain investments in the relational DBMS for reasons such as:

  • Performance. The performance of relational databases has been perfected to support the world’s most demanding data-centric services, including modern capabilities like caching and in-memory techniques.
  • Reliability. With a long history of successfully supporting the world’s largest governments and businesses, the relational database has proven to be trustworthy and reliable.
  • Integration. Relational databases have supported countless transactions since the 1980’s, meaning virtually every system within a government or enterprise is already integrated with the technology. 
  • Security. Today’s relational databases perform like security veterans, having matured over the 30 years that they have been protecting the world’s most sensitive data.
  • Skills. Professionals have developed and honed their RDBMS and SQL skills for decades.

Use cases for relational databases

Online transaction processing

OLTP applications are focused on transaction-oriented tasks that run at high rates. Relational databases are well suited for OLTP apps because they support the ability to insert, update or delete small amounts of data; they accommodate a large number of users; and they support frequent queries and updates as well as fast response times.

IoT solutions

Internet of Things (IoT) solutions require speed as well as the ability to collect and process data from edge devices, which require a lightweight database solution. Relational databases can offer the small footprint that is needed for an IoT workload, with the ability to be embedded into gateway devices and to manage time series data generated by IoT devices.


Data warehouses

In a data warehousing environment, relational databases can be optimized for OLAP (online analytical processing) where historical data is analyzed for business intelligence. A dimensional approach is used to facilitate queries on large numbers of records and the ability to summarize the data in multiple ways. Data stored in the data warehouse usually originates from multiple sources as well.

Choosing a relational database and RDBMS

The relational database and relational DBMS have long served as an efficient solution for business data to be stored and queried, and their capabilities continue to evolve to support next-generation applications. Key features to look for in a relational DBMS include:

  • The ability to embed in gateways and routers
  • Time-series data support
  • Small footprint
  • Low administration requirements
  • Options for hybrid multicloud database deployments
  • Integration with data warehouses
  • Support for AI and cognitive initiatives

Relational databases resources

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