What is a Relational Database and Why Do You Need One?

Databases are the silent workhorses powering virtually every modern organization. From small businesses to global enterprises, databases are so ubiquitous that we often take them for granted.

Yet, understanding how these crucial systems work and why they're so important is key to leveraging their full potential. This article aims to demystify one of the most common types of databases: the relational database. We'll explore its basics, benefits, and why it continues to be a foundation of data management for businesses worldwide.

A relational database is a type of database that organizes data into tables, which are structured with rows and columns. Each table, also called a relation, represents a specific person or idea. Rows in a table are for individual records, and columns are for the details of those records.

Relational databases provide a structured and efficient way to store, retrieve, and manage large volumes of data. They ensure data integrity and consistency through relationships between tables and the use of primary and foreign keys. Additionally, their use of Structured Query Language (SQL) enables powerful querying capabilities, making it easier to handle complex data operations and analysis. This means businesses can store data confidently, retrieve it quickly, and make smarter business decisions.

If you're losing time and money trying to sift through endless data, you may be a prime candidate for a relational database. This guide will break down the benefits of relational databases and how they'll help your business stay competitive.

If you want to see the power of a relational data model, look no further than the mighty spreadsheet. If you're comfortable using spreadsheets to sort rows of data, you already have an idea of why relational database models are so effective.

In the same way, relational databases work by dividing several forms of data into intuitive tables to structure information. They provide a standard way of representing and querying data that can be used by any application. Structured Query Language (SQL) is the most widely used language for database queries.

In the relational database model, data structures (such as data tables, indexes, and views) also remain separate from physical storage structures.

While similar to a spreadsheet in terms of organization, a relational database is much more complex. These structures allow businesses to gain valuable insight into their business performance and the relationship between large volumes of data.

Relational databases frequently organize data into a table (sometimes called a "relation"). Each table will represent a specific entity in the business, such as customers or specific orders.
For example, a business that works in skincare may have several tables dedicated to different customers and their orders. These tables could focus on the quantity of orders or customers buying a specific product and consist of rows and columns (also known as records and attributes, respectively). Each row will have a unique record, while every column has a specific attribute.

To continue the metaphor above, a skincare business has a table dedicated to multiple customers. That table could then have columns that state the purchasing activity of products for acne, eczema, and dry skin. Each row could contain a unique record, such as requesting a refund.

The biggest appeal of advanced database systems is how easy it is to retrieve information in a short amount of time. SQL is currently the standard language for interacting with a relational database.

Known as Structured Query Language, this language allows database administrators to perform operations such as updating, managing, and querying data. SQL is standardized by both ANSI and ISO, ensuring you'll always have compatibility across different relational database management systems.

Raw data is incredibly hard to sift through (and quite time-consuming, to boot). Primary and foreign keys provide identification that ensures data doesn't get mixed up with each other. They can also reduce data redundancy (or too many copies of the same data).

A primary key is a unique identifier for each record in a table, ensuring that no two rows can have the same primary key-value. It uniquely distinguishes each row in a table.

A foreign key is a field (or group of fields) in one table that identifies a row in another table. This makes it possible to link the two tables. This relationship keeps the referenced table's primary key the same; the value in the foreign key field must match a value in the referenced table's primary key.

A key concept in relational databases is the transaction. In database terms, a transaction is a sequence of one or more operations performed as a unit of work. Think of it as a set of instructions that need to be completed together for the database to remain accurate and consistent.

What is a Database Transaction?

Imagine you're transferring money from your savings account to your checking account. This process involves two steps:

1. Deducting money from your savings account
2. Adding that same amount to your checking account

In a relational database, these two steps would be executed as a single transaction. This ensures that either both steps complete successfully, or neither does. You wouldn't want the money to leave your savings account without appearing in your checking account, or vice versa!

Why are Transactions Important?

Transactions are crucial for maintaining the integrity and accuracy of your data. They ensure that your database remains in a consistent state, even when multiple operations are being performed simultaneously or if something goes wrong during the process.

Here are some key implications of transactions in relational databases:

1. Data Integrity: transactions help maintain the accuracy and consistency of your data. If any part of a transaction fails, the entire transaction is rolled back, preventing partial updates that could lead to data inconsistencies.
2. Concurrency Control: transactions allow multiple users to work with the same data simultaneously without interfering with each other. This is especially important in systems where many users need to access and modify data at the same time.
3. Error Recovery: if a system failure occurs in the middle of a transaction, the database can recover to its previous consistent state. This prevents data loss and ensures the reliability of your database.
4. Simplifies Complex Operations: transactions allow you to group multiple operations together, simplifying complex processes and ensuring they're executed correctly as a unit.

By using transactions, relational databases provide a robust and reliable way to manage your data, ensuring it remains accurate and consistent even in complex, high-traffic environments.

While a relational database stores information in tables, columns, and rows, a non-relational database (sometimes called a NoSQL database) is less structured. However, this doesn't necessarily mean it's a worse (or better) option for your business.

Let's start from the top. Relational databases are heavily structured and have a high amount of data integrity. These databases are very good at ensuring data isn't duplicated, lost, or misused. They provide businesses with a high level of security and offer very fast response times. Relational databases also use a fixed schema. For any application that requires a high volume of complex queries or transactions, they're the best fit.

A non-relational database can also provide a high amount of data, but with less organization. This approach keeps them flexible and scalable for businesses that are growing too fast for limitations. Non-relational databases store data in various formats such as key-value pairs, documents, wide-column stores, or graphs, depending on the specific type of NoSQL database.

This flexibility allows them to handle unstructured or semi-structured data efficiently. Unlike relational databases, NoSQL databases can easily scale horizontally, adding more servers to manage increasing loads, making them ideal for large-scale data applications. They also let you design your data model in a way that changes as you change it. This is especially helpful for fast development and changing data needs.

A non-relational database is most suitable for businesses dealing with large volumes of different data types, such as social media platforms, e-commerce sites, or content management systems.

For example, an e-commerce company that needs to store customer profiles, product catalogs, user-generated content, and transactional data can benefit significantly from a NoSQL database.

The dynamic schema and horizontal scalability enable the company to handle a high volume of reads and writes efficiently, support a wide range of data formats, and quickly adapt to changing business needs without significant downtime or restructuring.

A relational database appears quite similar to a vector model since they're both find relationships between large volumes of data. What makes them different is mostly in how they organize data points as well as the type of data.

Vector databases work with both structured data and unstructured data (though usually unstructured). With the aid of vector embeddings, any user can quickly retrieve data. More to the point, they're able to find insightful connections between data points.

While a relational database model still uses a spreadsheet-like structure, vector databases turn data into numerical representations. As a result, vector databases are also useful for training artificial intelligence and machine learning programs. These data points help create digital tools that are more subtle in how they understand human behavior.

Databases are versatile tools that can be applied in numerous ways to solve different data management challenges. Understanding these application patterns can help you better leverage databases for your specific needs. Here are some common patterns:

1. Transactional Systems (OLTP): Online Transaction Processing (OLTP) systems are designed to manage transaction-oriented applications. These are typically used in day-to-day operations where quick processing of numerous small transactions is crucial. Examples include banking systems, e-commerce platforms, and reservation systems.
2. Analytical Systems (OLAP): Online Analytical Processing (OLAP) systems are optimized for complex queries and data analysis. They're often used in business intelligence applications to analyze large volumes of historical data for insights and trends. Data warehouses typically fall into this category.
3. Content Management Systems: Databases are fundamental to content management systems (CMS), storing articles, media files, user data, and metadata. This pattern is common in publishing platforms, wikis, and document management systems.
4. Search Engines: While not always traditional relational databases, specialized database systems power search engines. They're designed to handle large volumes of data and provide fast, relevant search results.
5. Caching Systems: Some databases are used as high-speed caches to store frequently accessed data. This pattern improves application performance by reducing the load on primary data stores.
6. Time-Series Data: Databases optimized for time-series data are used in applications that deal with large volumes of time-stamped information, such as IoT sensors, financial trading systems, and monitoring tools.
7. Graph Databases: These specialized databases are used to store and query interconnected data. They're particularly useful for social networks, recommendation engines, and fraud detection systems.

Understanding these patterns can help you choose the right database solution for your specific needs. Many modern database management systems, like InterSystems IRIS, are designed to support multiple patterns, allowing for flexible and efficient data management across various use cases.

Relational databases are an incredible asset for businesses due to how effectively they store and organize data. Businesses can get deeper insight into their customers or clients, helping them make smarter decisions down the road. Below are just a few benefits you'll enjoy when using a relational database management system.

Relational databases are helpful for making sure data is always accurate and consistent throughout the database. When just one lost piece of data can cause a ripple effect throughout the business, these databases save time and money. This data integrity stays constant through constraints and rules.

It also has what's known as "referential integrity." This term refers to how the database maintains logical relationships between each table. Foreign keys can correctly reference primary keys, helping users draw accurate conclusions between sets of data.

Another advantage of a relational database system is how complex and flexible your queries are. Thanks to SQL, you'll be able to execute complex queries that can join multiple tables, aggregate data, and filter results. Likewise, users can create queries on the fly. They can retrieve specific data as needed, saving time that can be allocated to other business operations.

There are many tools to help you get into databases and analyze data. These include open-source business intelligence platforms, data visualization tools, and Extract, Transform, Load (ETL) solutions that can manipulate data in various ways. Popular open-source options include Apache Superset for business intelligence, Grafana for data visualization, and Apache NiFi for ETL processes. These tools enable users to interact with databases more easily, transforming raw data into actionable insights and visually appealing reports without requiring advanced SQL knowledge.

When a business is rapidly expanding, it's time-consuming and tedious to find new data storage solutions. Relational databases are able to handle very large volumes of data as well as numerous concurrent users.

Its fast performance also makes complex tasks like indexing, caching, and query optimization much more efficient. Data retrieval isn't only more accurate, it doesn't take as long.

Recent studies found data breach costs increased from $3.86 million to $4.24 million, a number that may not slow down anytime soon. Relational database systems offer a higher level of security that can give your users — and customers — peace of mind.

The relational model provides granular access control systems. These allow administrators to more clearly define user roles and permissions, restricting access to sensitive data.

Similarly, data encryption features help to protect data that's both at rest and in transit. Whether the data is unused or being queried, you can rest easy knowing it's unlikely to fall into the wrong hands.

Last but not least, the relational model provides data independence to reduce the risk of costly errors. There are two kinds your business will benefit from – logical data independence and physical data independence.

Logical data independence ensures that changes to the logical schema won't affect physical data storage. On the other hand, physical data independence ensures changes to physical storage won't affect the logical schema. This approach gives you greater flexibility when storing data, managing your database and making changes. If a mistake happens, you can mitigate the damage more easily.

While relational database management systems are quite helpful for businesses with large amounts of data, they're not perfect. Smaller businesses and start-ups may find the complexity and lack of scalability to be a challenge.

Relational databases have a complex schema design, requiring careful planning to run effectively. Compare this approach to, say, a data lake — a straightforward and unstructured collection of raw data.

The time-consuming and hands-on nature of a relational database can be too much for smaller business models. Making sure that normalized tables aren't unnecessary and maintain integrity can also be a challenge.

Relational databases aren't optimized for storing and querying unstructured data. As such, you won't be able to store text documents, images, or multimedia.

If your business regularly handles large volumes of different kinds of data, a data lake or vector database may be more appealing.

While relational databases aren't impossible to scale, they may not suit a rapidly evolving business. Relational databases can scale vertically (such as adding more resources to a single machine). However, they face limitations when scaling horizontally (such as distributing data across multiple machines).

You have plenty of relational database management systems to choose from, but some are better suited to certain business models.

A very large business may prefer one for its multimodal approach, while a smaller business may require flexibility.

• MySQL: this open-source RDBMS is popular for its reliability, performance, and ease of use.
• PostgreSQL: their advanced open-source RDBMS is well-known for its robust features and close compliance with SQL standards.
• InterSystems IRIS: our database management system provides a multimodal approach to support businesses at scale.

InterSystems IRIS provides you with more than just relational databases. Our multimodel database management system supports various data models such as relational, document, key-value, and graphs.

This means it not only supports the creation of relational databases, but also provides capabilities to handle different types of data all within a single platform.

With robust support for SQL and advanced query capabilities, InterSystems IRIS enables businesses to define and control relational data efficiently.

The platform's flexibility in schema management allows for seamless adjustments as business needs evolve, ensuring that the database structure remains aligned with your organizational goals.

A key advantage of InterSystems IRIS is its emphasis on performance optimization, especially with large-scale data. Unlike solutions like PostgreSQL that struggle with growing datasets, InterSystems IRIS maintains high performance even when processing terabytes of data. Through features such as intelligent data sharding and optimized query execution, InterSystems IRIS ensures your database performance scales linearly with data growth.

This performance focus is complemented by strong high-availability features, including clustering and data replication. These features ensure that there is little downtime and that important data can be accessed without interruption.

InterSystems IRIS also offers complete security measures, with role-based access control and data encryption. These measures protect your important information and help you follow the rules.