A relational database stores information as structured data consisting of related tables. Each table represents an entity with rows and columns. The rows of the tables represent records, while the columns in the table represent fields or attributes associated with that table. Entities are related to each other through key value fields or columns. The key-based references establish logical connections between tables, called relations, giving rise to the term relational model.
The Relational Data Model
In the early 1960’s, each application used its own unique structure to store data. This made it difficult for developers to build applications that could use the data, as they needed to know a lot about the particular data structure to access the information they needed. These structures were inefficient, hard to maintain, and not optimized for good application performance.
To solve this problem, the relational database model was introduced by Edgar F. Codd in 1969. He developed a standard way of representing and querying data that could be used by any application. The key strength of the relational model was its use of tables, which were an intuitive, efficient, and flexible way to store and access structured information.
Imagine you have many boxes in your garage, all filled with different things. A relational data model is like organizing those boxes neatly and efficiently. Here’s the breakdown:
- Boxes are like tables in a database. Each table holds information about a specific kind of thing, like “customers,” “products,” or “orders.”
- Things in the boxes are like records in a table. Each record represents one individual item, like a specific customer, product, or order.
- Labels on the boxes are like columns in a table. Each column represents a specific property of the things in the box, like “customer name,” “product price,” or “order date.”
- Relationships between boxes: Now, things in your garage might be related. Tools might go with toolboxes, and toys might go with specific children. In a relational database, these connections are made with foreign keys. Imagine drawing lines between boxes to show which things belong together.
This organized system makes it easy to find things. You know exactly which box to look in and what label to check. Similarly, with a relational database, you can quickly query and retrieve specific information using the table structure and relationships.
Why are Relational Databases Important?
Relational databases have become the predominant storage choice for transaction processing and data warehousing, where managing large amounts of structured data is essential. They support applications demanding complex querying, transaction processing, and consistent data. As such, they are widely used across enterprise resource planning (ERP) applications such as SAP and Oracle, customer relationship management (CRM) applications like Salesforce and Hubspot, financial transactions apps, and e-commerce apps. Relational databases use Structured Query Language (SQL) – a popular programming language to query and manipulate data. Data Engineers, data scientists, software developers, computer science graduates, and most application programmers are familiar with SQL. The relational data model and the associated data structure are relatively easy to learn and master, making them a natural choice for organizing data for retrieval and management. According to estimates, in 2024, over 290,000 businesses are utilizing relational databases.
Key Features of a Relational Database Management System (RDBMS)
RDBMS boasts several key features that have made it the cornerstone of data management for decades. Here are some of the most important:
Data Organization
Tables: Data is stored in tables with rows and columns, where each row represents a record, and each column represents an attribute of that record. This structure organizes data clearly and facilitates efficient retrieval.
Relationships: Tables can be linked through relationships defined by primary and foreign keys, allowing you to connect related data across different tables. This enables complex queries and analysis.
Data Integrity and Consistency
ACID properties: RDBMS adheres to the ACID properties (Atomicity, Consistency, Isolation, Durability), ensuring data integrity. This means transactions are complete, data adheres to defined rules, changes are isolated from other users, and updates are permanent.
Data types and constraints: Defining data types for each column and setting constraints like primary and foreign keys help maintain data consistency and prevent errors.
Data Management and Security
SQL: Standardized SQL (Structured Query Language) provides a powerful and universal language to interact with the database, manipulate data, and perform complex queries.
User access control: You can define different user roles and permissions, granting access to specific tables or data subsets based on individual needs.
Additional Features
Data dictionaries and metadata: RDBMS stores metadata describing the database structure, which aids in data management and understanding.
Transactions: You can group multiple data modifications into a single transaction, ensuring all changes succeed or fail together and maintaining data integrity.
Scalability: RDBMS can scale to accommodate growing data volumes by adding more storage or distributing data across multiple servers.
While these are some of the core features, it’s important to remember that specific functionalities may vary depending on the chosen RDBMS software.
Actian Relational Databases
The Actian Data Platform includes multiple relational databases, including Vector for high-speed analytic workloads and Actian Ingres for Transactional workloads. DataConnect provides an intelligent, low-code integration platform to address complex use cases with automated, intuitive, and reusable integrations.
The Actian Data Platform runs on-premises and on multiple cloud platforms, including AWS, Azure, and Google Cloud, so you can run your analytics wherever your data resides.