Relational Database: Definition, Examples, and More

Key takeaways

• A relational database is a type of database for storing and accessing data items that have a predetermined relationship with one another.

• A relational database handles indexing by keeping those logical data structures completely separate from physical storage.

• A relational database management system is a program that enables you to create, update, and perform administrative tasks with a relational database.

When you work with relational databases, you use SQL to retrieve, filter, and identify the information you need. Discover more about relational database features, use cases, and how to work with them in the following article. Afterward, if you’ve decided to begin your career journey in this field, enroll in the IBM Data Warehouse Engineer Professional Certificate. In as little as four months, you can learn about relational databases, data transformation, data architecture, database design, database management, and more.

What is a relational database?

A relational database is a digital repository that stores related data in rows and columns. These types of databases are "relational" because the data items within them have pre-determined relationships with one another.

Relational databases store data in tables. Unique IDs or “keys” connect the tables. When you need to access specific information, you can use a key to retrieve all predetermined tables of data associated with that key.

Relational database example use case

Suppose you're working for a company that sells clothes online. Your organization uses a relational database to manage data related to shipping, customer information, inventory, and website traffic. You have a key to this database that grants access to all shipping-related tables, and you need to determine whether you have sufficient inventory to fulfill last week's orders.

Since the relational database recognizes a relationship between shipping information and inventory, you can use your key to access inventory numbers and shipping requests to compare the data. During this request, you won't access any information about website traffic, as your key only grants access to the shipping-related data tables.

Watch this video to get an introduction to relational databases and preview IBM's Data Engineering Professional Certificate:

Relational database management system (RDBMS)

An RDBMS is a program that enables you to create, update, and perform administrative tasks with a relational database. The difference between a relational database and an RDBMS is that relational databases use a relational data model to organize information, while an RDBMS is database software that allows you to maintain the database.

Example: Common examples of relational database management systems include MySQL, Microsoft SQL Server, and Oracle Database. For instance, an organization could use Microsoft SQL Server, a relational database management system, for enterprise resource planning (ERP), customer relationship management (CRM), and human resources (HR).

How do relational databases work?

Developers build relational databases on relatively simple concepts, such as processing requests and organizing related data points, which can become increasingly complex as the data they contain grows. Read on to learn more about how relational databases work.

Processing requests and retrieving information

In an RDBMS, you input SQL queries to retrieve the data needed for specific job functions. SQL stands for structured query language. It's a standardized method for requesting information from relational databases.

Example: It's similar to the way you might type your question into Google, but different from how you'd ask a friend for the same information. Instead of saying, "What's that funny rap song from the Sonic the Hedgehog 2 movie?" you might type in "Sonic the Hedgehog 2 soundtrack list." This formatting change makes it easier for the algorithm to pull the data you need immediately.

Read more: Why and How to Learn SQL

Organizing related data points

Each row in a relational database table has an access key, and each column has data attributes. The attributes have values that help you understand the relationships between data entries.

Example: A relational database for a shoe store has two tables with related data. In the first single table, each record includes columns containing the customer’s billing and shipping information. The relational database assigns a key to each row. The second single table contains the customer's order information (product, size, quantity). The keys from the first table appear in a list alongside the order information in the second table because the database recognizes their relationship. This setup makes it easy for the warehouse to pull the correct product from the shelf and ship it to the right customer.

You can gain hands-on practice working with relational databases and SQL through the IBM Data Engineering Professional Certificate on Coursera. Master relational database management for data storage and retrieval. Learn about SQL and apply database administration skills.

Relational vs. non-relational database

Non-relational databases do not store data in rows and columns like their relational counterparts. Instead, non-relational databases store information in an optimized way tailored for the specific data being stored. For example, non-relational databases may store information in key-value pairs or graphs. Non-relational databases are called NoSQL databases because they do not use SQL for queries. Because of their scalability, use cases for NoSQL databases include big data applications, machine learning workloads, e-commerce, expansive digital assets, and mobile applications.

Relational database features

Relational databases provide several features, such as data consistency, isolated transactions, and data protection in the event of a system failure. You can use these database types to process and manage transactions. Retail, banking, and entertainment industries often use relational databases, where an exact amount of money, tickets, or products is withdrawn from one location or account and deposited into another. The acronym ACID can represent transactions with these properties, and it stands for:

• Atomicity: All parts of a transaction execute completely and successfully, or else the entire transaction fails.

• Consistency: Data remains consistent throughout the relational database. It enforces data integrity, or the accuracy and completeness of the data at hand, using integrity constraints, similar to rule enforcers.

• Isolation: Each transaction is independent of other transactions. Data from one record does not spill onto another, so it is secure.

• Durability: Even if the system fails, data from completed transactions is safely stored.

By taking the relational approach to data queries, data and business analysts can perform specific functions to obtain the information they need to organize query results by name, date, size, and location. This relational model also means that the logical data structures, such as tables and indexes, are completely separate from physical storage.

Why is a relational database important?

A relational database’s main benefit is the ability to connect data from different tables to obtain useful insight. This approach helps organizations of all sizes and across industries decipher the relationships among data sets from various departments.

How you structure data within a relational database can also be useful for managing access permissions. Since the relationships between data points are predetermined and require a unique ID for reference, you obtain only relevant, pre-screened information.

Benefits of relational databases

Other benefits of relational databases include simplicity, ease of navigation, and cost-effectiveness. Explore these advantages below:

• Simple and centralized database: Relational databases are simple. Toggling between tables provides a wealth of information for various purposes. Plus, enterprise resource planning (ERP) systems, being built on relational databases, help you manage clients, inventory, and much more.

• Easy to use: Many companies use relational databases and ERP to organize and manage large amounts of data. Their continued use helps to drive improvements to these systems, such as migrating to the cloud. Using SQL, you can easily navigate data sets to retrieve, filter, and identify the information you need.

• Save time and money: By using relational databases, companies can stay organized and efficient. The unique IDs help eliminate duplicate information, whether it is tracking a customer’s order or a museum's visitors. Instead of taking time to enter customer data into logs, a relational database reduces redundancy, saving employees time. Companies can save money by allocating that labor elsewhere.

Careers that use relational databases

Careers that use relational databases include data engineers, data analysts, and data scientists, among others. Data engineers design and build relational databases (and other data management systems) to help organizations collect, store, and analyze data. Then, data analysts and data scientists use them to analyze large volumes of data and uncover meaningful insights. Explore in more detail how these professions and others utilize relational databases:

• Data engineer: Data engineers design and build systems for collecting and analyzing data. They typically use SQL to query relational databases to manage data and identify inconsistencies or patterns that may positively or negatively affect an organization’s goals.

• Database administrator: Database administrators act as technical support for databases, ensuring optimal performance by performing backups, data migrations, and load balancing.

• Data architect: Data architects analyze the data infrastructure of an organization to plan or implement databases and database management systems that improve efficiency in workflows.

• Data analyst: Data analysts take data sets from relational databases, clean and interpret them, and use them to answer business questions or solve problems. They can work in industries as varied as business, finance, science, and government.

• Data scientist: Data scientists take those data sets to find patterns and trends, and then create algorithms and data models to forecast outcomes. They might use machine learning techniques to improve data quality or product offerings.

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