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Glossary

Relational Database

A relational database stores and structures data by organizing it in rows, columns and tables, based on defined relationships between data points.

What is a relational database?

A relational database is software that stores data, organized into rows, columns and tables, based on defined relationships between data points. Each column holds attributes of the data, with each row being a record with a unique identifier or key. This makes it simple to understand the relationship between different points, making it easy to access and query structured information at scale. Different database tables can be linked and interrelated.

An example of a relational database

A relational database might contain a table for a company’s customer data. Each column will describe a different customer attribute (such as name, address, and telephone number), while each row will relate to the individual customer. Information is stored consistently under each attribute, making analysis and manipulation simple and straightforward.

Showing the power of relational databases, this table might be linked to a separate table containing sales orders, enabling businesses to easily track and analyze what customers have bought.

Where does the relational database sit in the tech stack?

The relational database is part of a database management system (or DBMS), which controls the storage, retrieval, addition, sorting, deletion, modification or use of the data contained within the database.

Database administrators use the well-known Structured Query language (SQL) programming language to store, process and query information in a relational database.

Where are relational databases used?

Relational databases provide a simple way to collect and share structured data between employees and business systems based on logical rules. First introduced in the 1970s, they standardized how data was represented and shared between applications – previously every application stored data in a unique way, limiting interoperability.

Relational databases are used by the majority of business applications, such as solutions for finance, accounting, Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP), which all rely on tabular, structured data. They therefore power activities such as ecommerce, banking, HR systems, inventory management and sales orders, as well as being used as a source for business intelligence analysis.

Relational databases are best suited to the storage of structured, quantitative data that follows and fits into specific, defined models and formats. They are unsuited to storing unstructured, qualitative data, such as images or videos, which do not fit into the row and column format. These are normally collected in NoSQL databases.

What are the benefits of relational databases?

Relational databases have four main benefits:

Ease of use

Because of their tabular format, relational databases are straightforward to understand, even by non-specialists. Given their long history, there is a large number of tools available to help manage them. As SQL is a widespread language it is known or can be learnt by most programmers and business analysts, while providing the ability for users to carry out complex queries.

Security and data confidentiality

Access to specific data can be limited to particular users, based on their jobs or roles. This ensures data confidentiality, particularly of personal information. Access can be set to the level of particular tables, or columns within a table. For example, information such as a customer’s stored credit card number could be hidden from users.

Easy to manage

The relational database schema is separate from how data is physically stored. This means that changes to physical data storage (such as renaming a table) do not affect the underlying data or existing applications.

ACID compliance

Relational databases support ACID (Atomicity, Consistency, Isolation, Durability) performance. Data is stored just once, eliminating duplication, while ensuring data validity regardless of errors or failures. Data is automatically updated between different instances of relational databases – for example, if money is withdrawn from an ATM, it is immediately reflected in the customer’s bank balance.

 

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