What is a relational database? Ultimate guide for 2026

There are several different types of databases on the market, some designed to handle specific data types or use cases, making it hard to determine which are the best to invest in to meet your business goals.

Relational databases have long been a reliable standard—and they still are—increasingly used along with other database types, and now with AI. But there’s a difference between legacy providers who’ve added AI to their database functionality and what AI-native companies can offer. That difference is often felt in speed to implementation and AI business app production, and easy collaboration and workflows across your organization.

What is a relational database?

The relational database model organizes information into tables with rows and columns, where different tables connect through defined relationships. This structure allows you to store data efficiently, minimize redundancy, and maintain data integrity across your systems.

Within a relational database, each table represents a specific entity—like customers, orders, or products. Rows (also called tuples) contain individual records, while columns define the attributes or data points for each record. What makes these databases "relational" is their ability to link information across different tables using keys.

For example, instead of repeating customer information in every order record, a relational database stores customer details once in a customer table and links to it using a unique identifier called a customer ID. This approach ensures data consistency and makes updates more manageable across your database structure.

Why are relational databases important?

Relational databases have long been the backbone of business operations because they provide a reliable and consistent way to store data. These databases excel at handling complex relationships between different types of data, whether you're tracking customer information, managing inventory, or analyzing sales patterns. Relational databases allow you to organize information logically and retrieve it using SQL (Structured Query Language), which is flexible enough to handle everything from transaction processing to application development.

These databases extend well beyond simple data storage and provide structured data management. That’s because they also enforce business rules through constraints and relationships, protect against data loss through ACID properties (atomicity, consistency, isolation, durability), and support multiple users accessing information simultaneously through concurrency controls.

How do relational databases work?

Relational databases work by organizing information into tables. Each table follows a predefined schema that specifies the types of data it can contain and how that data should be structured. This tabular format makes it easy to understand and work with information. Connections between tables are established through primary keys and foreign keys.

A primary key serves as a unique identifier for each row in a table, like an employee ID or order number. Then, a foreign key in one table references the primary key in another table, creating a relationship between them. For instance, an orders table might include a customer ID foreign key that links to the primary key in your customer table.

To retrieve information, you’ll use SQL queries to ask questions and pull information from your database. These queries can join data from multiple tables, filter results based on specific criteria, and perform calculations or aggregations. The relational database management system (RDBMS) processes these requests and returns the information you need, pulling from different tables as necessary to provide a complete picture.

What is a relational database management system (RDBMS)

RDBMS represents the software that creates, manages, and provides access to relational databases. This is the engine that powers your database, handling everything from data storage to query processing, security, and user management.

Popular RDBMS platforms include MySQL, PostgreSQL, Oracle, and Microsoft SQL Server. While they all follow the relational data model and use SQL, each has unique features, strengths, and use cases. Some are open-source, making them accessible for projects of any size, while others offer enterprise features like advanced analytics, high availability, and strong customer support options.

A brief history of relational databases

The relational database concept emerged in 1970 when IBM researcher Edgar F. Codd published a groundbreaking paper, “A Relational Model of Data for Large Shared Data Banks,” which introduced the relational data model. His work proposed organizing data in tables with relationships between them—a revolutionary idea that addressed limitations in earlier database approaches.

Throughout the 1970s and 1980s, companies like IBM, Oracle, and others developed commercial RDBMS products based on Codd's principles. The introduction of SQL as a standardized programming language for querying relational databases helped drive widespread adoption. By the 1990s, relational databases were widely adopted.

Today, the relational model remains fundamentally the same, but modern systems can handle much larger volumes of data, support distributed architectures, and integrate with cloud platforms. The rise of NoSQL databases in recent years has complemented relational databases, rather than replacing them.

Relational vs. non-relational databases

Relational databases and non-relational databases (often called NoSQL databases) store and organize data differently. Relational databases use structured tables with predefined schemas and enforce relationships through keys, while NoSQL databases offer more flexible data structures like documents, key-value pairs, graphs, or wide-column stores.

The structured approach of relational databases provides strong data integrity guarantees and supports complex queries across multiple tables. Relational databases excel at handling structured, relational data where consistency and integrity are paramount—think financial transactions, customer relationship management, or inventory systems.

Non-relational databases, by contrast, prioritize flexibility and scalability, and are often used when handling large volumes of unstructured data (e.g., email, social media, text documents) or in scenarios that require rapid scaling across distributed systems.

When to use relational database vs. NoSQL

Choose a relational database when your data has clear, stable relationships and you need strong consistency guarantees. If your application requires complex queries that join multiple tables, transaction processing with ACID properties, or you're working with structured data that fits naturally into tables, a relational database system is typically your best choice.

Relational databases work particularly well within many business applications and use cases: supporting operations, finance, human resources, sales, customer relationship management, and product development, where, for example, you need to connect insights across a product portfolio. They're ideal when you have well-defined data requirements and your schema won't change frequently.

Consider NoSQL databases when you're dealing with massive volumes of data that require horizontal scalability, where your data model needs to evolve rapidly, or are working with unstructured or semi-structured data. Many organizations use both types of databases, leveraging each for its strengths in particular use cases.

Benefits of effective relational database management

Effective relational database management delivers many benefits that impact your organization's efficiency and reliability.

What are the key features of a relational database?

Several key features distinguish relational databases and make them a powerful tool for data management.

Constraints: These enforce data integrity and are rules that ensure data quality by preventing invalid entries, maintaining relationships, and guaranteeing uniqueness where required. Constraints act as guardrails, catching errors early, before they corrupt your data.

Tables: Tables are the main building blocks of relational databases. Each table is a data set composed of records and fields, and represents a single subject. For instance, if you’re an event planner, you might have a table for events, a table for venues, a table for vendors, and a table for clients. Each of these tables contains all of the relevant data for that specific subject: for example, the event table might contain dates and times for each event. Inside a table, the data is arranged into records and fields.

Records: In a relational database, a table represents a single subject; a record is a unique instance of that kind of subject. For example, in an event planner’s database, each record in the events table represents a different event (“The City Museum 15th Annual Fundraising Gala,” “Equilux Holiday Party,” etc.); each record in the vendors table represents a different vendor (“Objectively Edible Catering,” “By the Bouq Flowers,” etc.); and so on.

Fields: A field represents a characteristic of the subject of the table. For example, in an event planner’s database, the “Venues” table might include fields like “Street address” and “Maximum occupancy,” whereas the “Vendors” table might include fields like “Vendor type” and “Vendor Phone Number.”

Primary keys: Within a table, the primary key field—or as we call it at Airtable, the primary field—holds a unique identifier ID for each record. The primary field cannot be deleted, and must contain unique information. For this reason, it can’t be formatted as a checkbox or a select field. It must contain a unique identifier—words or numbers.

Foreign keys: A foreign key field is a field within one table that points to the primary key field of another table. For instance, in your table of customers, you might have a field containing a unique identification number for each customer, and that ID number will be the primary key field of the customers table. When that ID number is pulled into another table—such as a customer orders table—it shows up in that table as a foreign key field.

Relationships: The primary and foreign key fields create relationships between database tables. To say that tables are related is to describe how they share information. When information is updated in a record, it’s automatically updated in every table that displays that information.

ACID properties: ACID stands for Atomicity, Consistency, Isolation, and Durability. Relational databases support these properties to guarantee reliable transaction processing. Atomicity ensures that transactions complete fully (or not at all), consistency maintains database rules, isolation prevents concurrent transactions from interfering with one another, and durability guarantees that committed changes persist even through system failures.

Database schema: As you architect your database, you create a structure with rules. This is called a schema. It’s the blueprint for your database that describes how you will store information, how that information will be related, and how a database administrator can work with it later. A database schema allows you to enforce rules around data types. For instance, you might decide that a field only has three possible options, which you’ll input as strict choices. Or you might indicate that a field can only take text, or only numerals, or only an image file.

SQL: The use of SQL provides a powerful, standardized way to interact with your data, and it’s a widely known querying language. SQL queries let you retrieve exactly the information you need, join data from different tables, perform calculations, and modify data—all through declarative statements that describe what you want to know.

AI and workflow automations: Modern relational databases increasingly incorporate AI and automation tools to streamline operations and reduce manual work. Automations can run tasks in response to triggers—such as creating a new record, updating a field, or reaching a specific date—allowing routine processes to execute reliably in the background. AI extends this further by helping interpret data, generate insights, or assist with tasks like summarizing information, categorizing records, or predicting future values. Together, AI and workflow automations ensure that your database doesn’t just store information—it actively supports your processes, reduces repetitive work, and helps your team move faster with greater accuracy.

Relational database example: Airtable

Airtable demonstrates the power and accessibility of modern, cloud-based relational databases. Unlike traditional RDBMS platforms that require technical expertise, Airtable brings relational database capabilities to teams across your organization through an intuitive interface that doesn’t require any technical expertise. Your marketing, sales, operations, HR, finance, or product teams don’t need to have development skills to benefit from the results a relational database can deliver.

In Airtable, you create bases that contain multiple tables, just like in any relational database. You can link records between tables using linked record fields—essentially foreign keys that create relationships. For instance, you might have a Projects table linked to a Teams table, showing which team members work on each project.

Airtable stands out because it handles relational data management without requiring SQL knowledge. You can create lookup fields that pull information from related tables, use rollup fields to calculate values across linked records, and build views that present your data in different formats—from grids to Kanban boards to calendar views—catered to the way different teams work.

And with AI at the ready, Airtable expands what’s possible with relational data. AI can help you summarize project updates, generate content, classify records, or uncover insights across connected tables. When paired with Airtable’s automations, AI enables your workflows to run intelligently in the background, turning your relational database into an active system that supports decision-making, reduces manual work, and helps every team move faster and with greater clarity.

How relational databases are structured

The structure of relational databases follows clear organizational principles. At the highest level, a database contains multiple tables, each representing a distinct entity or concept (e.g., customers or employees, orders or products) within your data model. These tables follow a schema that defines column names, data types, and constraints to ensure that information is stored in a consistent way.

Within each table, rows represent individual records or instances of the entity. If you have a customer table, for example, each row represents one customer with their associated data points. Columns define the attributes—name, email, phone number, and so forth. Each intersection of a row and column contains a single value, following the principles of normalization to minimize redundancy.

Relationships connect tables through primary and foreign keys. A primary key uniquely identifies each row within its table, while foreign keys in related tables reference these primary keys. For example, an orders table might include a customer ID foreign key that points to the customer ID primary key in your customer table. This structure lets you maintain customer information in one place while linking it to multiple orders, avoiding duplication of customer information and providing the ability to see all orders made by any customer.

What to look for when selecting a relational database

Selecting the right relational database requires evaluating several key factors:

What is the most effective relational database for collaboration?

While traditional RDBMS platforms like PostgreSQL and MySQL offer strong data management capabilities, they typically require database administrators to manage them and aren't designed with collaboration in mind. For teams that need to work together with relational data without technical barriers, the most effective platforms are those designed for collaborative data management.

Airtable is powered by a relational database but with built-in collaboration features. Multiple team members can work simultaneously, comment on records, and receive updates in real-time—all without writing SQL queries or needing to understand database administration.

Collaboration features include shared views, commenting, revision history, and granular permissions that allow teams to work together effectively in the ways that they each work best. As a low-code platform, Airtable democratizes database access for easy data management, faster decision-making, and more efficient collaboration across your organization.

Try Airtable’s relational database for free

Airtable provides the data integrity, relationships, and structure of traditional relational databases paired with the accessibility, collaboration, and AI features that modern teams need.

Discover how Airtable can transform how your team organizes and uses information to collaborate. Get started today and see how easy it is to harness the power of a no-code relational database.