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

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What is a relational database? Why is it important to planning and operations? All this and more, in this quick guide to relational databases.

[Featured image] A data engineer in a grey sweater and headphones sits at her computer running a query on a relational database.

For a modern art museum in Stockholm, a relational database 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 analyzing data. They can work in many industries to help all types of organizations collect data. Other job roles, such as data analysts and data scientists, are then able to 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 the world of 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.” Using this key, users can unlock data entries related to that key on another table, to help with inventory management, shipping, and more. On 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. In the columns are data attributes. Each record has a value for each attribute, so users can understand the relationships between data entries for functions like product marketing, manufacturing, UX research, and more. 

As an example, for a shoe store processing online orders, a relational database might have two tables with related data. 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, color, and more. The two tables are related, and toggled to each other, with the key. When an order comes in, the key allows the warehouse to pull the correct product from the shelf and ship it to the customer. 

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Why is a relational database important?

A relational database’s main benefit is the ability to connect data from different tables to create useful information. This approach helps organizations of all sizes and industries decipher relationships between different sets of data, 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 centralized database: Relational databases are simple. Toggling between tables provides a wealth of information that can be used for various purposes. Plus, 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 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, users can easily navigate data sets to retrieve, filter, and ideate the information they 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 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 labor 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 product) 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 else 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 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.

Careers that use relational databases

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

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

3. 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.

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 take those data sets to find patterns and trends, and then will create algorithms and data models to forecast outcomes. They might use machine learning techniques to improve the quality of data or product offerings.

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