Relational Database: What It Is and Why It’s Important

A relational database for a modern art museum in London can maintain data for the art collection, allowing curators to track the artists, their collections, and potential buyers. That same art museum could have a database dedicated to registered visitors to track when and how often they return.

Data engineers design and build systems (including relational databases) for collecting, storing, and analysing data. They can work in many industries to help organisations collect data. Other job roles, such as data analysts and data scientists, can tackle this large amount of data and derive meaningful insights from it.

Here’s a quick guide to relational databases. You’ll learn what they are and why they’re important in data science.

What is a relational database?

Relational databases are a type of database that allows users to access data that is stored in various tables connected by a unique ID or ‘key’. Users can use this key to unlock data entries related to that key on another table to help with inventory management, shipping, and more. In relational database management systems (RDBMS), users can input SQL queries to retrieve the data needed for specific job functions.

In a relational database, each row in the table has a key. The columns contain data attributes. Each record has a value for each attribute so that users can understand the relationships between data entries for functions like product marketing, manufacturing, UX research, and more. 

For example, a relational database might have two tables with related data for a shoe store processing online orders. In the first table, each record includes the customer’s name, shipping address, email, and billing information in columns. A key is assigned to each row. In the second table, that key is listed alongside the product ordered, quantity, size, colour, and more. The two tables are related and toggled to each other with the key. When an order arrives, the key allows the warehouse to pull the correct product from the shelf and ship it to the customer. 

Guided project: Learn how to use a relational database

In this guided project, Introduction to Relational Database and SQL, exclusively from Coursera, gain hands-on experience working with Oracle’s MySQL Workbench. You can create a billing report and a club member roster.

Why is a relational database important?

A relational database’s main benefit is connecting data from different tables to create useful information. This approach helps organisations of all sizes and industries decipher relationships between different data sets from various departments to create meaningful insights. 

Benefits of relational databases

Relational databases provide plenty of benefits for companies. Here are a few primary advantages of relational databases:

• Simple and centralised database: Relational databases are simple. Toggling between tables provides a wealth of information that can be used for various purposes. ERP systems are built on relational databases, so they help users manage clients, inventory, and much more.

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

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

Features of relational databases

Relational databases tend to be used for processing and managing transactions. They are often used in retail, banking, and entertainment industries, where an exact amount (of money, tickets, or products) is withdrawn from one location or account and deposited into another. Transactions like these have properties that can be represented by the acronym ACID, which stands for:

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

• Consistency: Data remains consistent throughout the relational database. Data integrity, or the accuracy and completeness of the data at hand, is enforced in relational databases with 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, analysts can perform specific functions to obtain the information they need to organise 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.

Careers that use relational databases

1. Data engineer:  Data engineers design and build systems for collecting and analysing data. They typically use SQL to query relational databases to manage the data and look for inconsistencies or patterns that may positively or negatively affect an organisation’s goals. 

2. Database administrator: Database administrators provide technical support for databases and ensure optimal performance by performing backups, migrations, and load balancing.

3. Data architect: Data architects analyse an organisation's data infrastructure to plan or implement databases and database management systems that improve workflow efficiency.

4. Data analyst: Data analysts take data sets from relational databases to clean and interpret them to solve a business question or problem. They can work in industries as varied as business, finance, science, and government.

5. Data scientist: Data scientists analyse data sets to find patterns and trends and create algorithms and data models to forecast outcomes. They might also use machine learning techniques to improve data quality or product offerings.

Get started with Coursera.

Learning about relational databases can be your key to a career in data science. Explore relational data models, how they’re created, and what benefits they bring through hands-on exercises with real-world examples. Get started on Coursera with the Introduction to Relational Databases (RDBMS) from IBM, a course you can complete in 16 hours or less.