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. The addition of the object relational model, which is similar to a relational database, has also enabled vendors to offer extensions that support data types that are not part of the SQL standard, such as time series data.

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.

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Use cases for relational databases

Icon representing the online transaction processing, OLTP, use case 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.

Icon representing the Internet of Things use case for relational databases

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.

 

Icon representing the data warehouse use case for relational databases

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 admin requirements
  • Options for hybrid cloud database deployments
  • Integration with data warehouses
  • Support for AI and cognitive initiatives

 

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