What is a relational database?
Relational databases are a type of database that store and organize data points with defined relationships for fast access. With a relational database, data is organized into tables that hold information about each entity and represent pre-defined categories through rows and columns. Structuring data this way makes it efficient and flexible to access, which is why relational databases are most common. Relational databases are also built to understand Structured Query Language (SQL), a standardized programming language which is used to store, manipulate, and retrieve data. Within SQL, there's a built-in language for creating tables called Data Definition Language (DDL) and a language for manipulating data called Data Manipulation Language (DML).
What does relational mean? Relational means indicating or constituting relation. In the context of databases, the way we define relational applies primarily to the data itself. Datasets that are relational have pre-defined relationships between them. For example, a database that includes customer information for a company may also include individual transaction data attached to each account. Relational databases focus attention on the relation between stored data elements.
Characteristics of relational databases:
Relational databases consist of multiple entities
Standard Query Language (SQL) is the standard interface for relational databases
Relational databases are highly structured and represented using a schema (logical and physical)
Relational databases reduce data redundancy
How relational databases work
Relational databases typically use tables with data organized into rows (containing entities) and columns (containing entity attributes). This process is known as normalization. Each row holds a unique identifier or key that ties tables together to establish a relationship. When a relational database is queried, the key is used to find related data across datasets. For instance, a technology help desk might want to track customer interactions by issue type, time to resolve the issue, and customer satisfaction. Within this database, what creates a relationship and makes the table structure function well is the unifying customer ID.
Examples of relational databases
Relational databases are useful for any information need where data points both relate to each other and also must be managed in a consistent, secure, rules-based way. This is what makes relational databases most popular for businesses and enterprises. When businesses want to draw insights from their own data, they rely on relational databases for generating useful analytics. Many of the reports businesses generate to track inventory, finance, sales, or make future projections are created using relational databases.
How is data in a relational database organized? Data in relational databases is stored, searched, and retrieved from tables with relationships. Within a relational database, the database schema defines how data is organized both logically and physically.
Relational databases have what's called a consistency mode or integrity that's based on four criteria: atomicity, consistency, isolation, and durability (ACID). Here's the value of each ACID database property:
- Atomicity defines elements that make up a complete transaction.
- Consistency defines rules for maintaining data integrity following a transaction.
- Isolation keeps the effects of transactions invisible to others so they don't contend with one another.
- Durability ensures data changes become permanent after each committed transaction.
These criteria make relational databases useful in applications that require high accuracy, such as financial and retail transactions, also known as online transaction processing (OLTP). Financial institutions rely on databases to track huge amounts of customer transactions—from balance inquiries to transfers between accounts. A relational database is ideal for banking since it's built to handle a large number of customers, frequent data changes from transactions, and fast response times.
Explore fundamental relational data concepts in this tutorial from Microsoft Learn.
What is a MySQL relational database?
My Structured Query Language (MySQL) is a common open-source SQL relational database that performs all the basic SQL commands, such as writing and querying data. A reliable, stable, and secure database management system (DBMS), MySQL is widely adopted because it supports most leading programming languages and protocols. In fact, MySQL is robust enough that it serves as the primary data store for many large organizations. MySQL is also suitable as an embedded database for software, hardware, and appliances.
Typically, MySQL relational databases include hardened and flexible security features such as host-based verification and password-encrypted traffic. Web developers often prefer MySQL since it's easy to use and it contains productivity features, such as updateable views, stored procedures, and triggers (special procedures that run when specific actions occur in the database server). MySQL is a popular transactional engines for e-commerce platforms because it's adept at managing things like transactions, customer profiles, and product inventory information. Designed to be highly compatible with other systems, MySQL also supports deployment in virtualized environments such as cloud platforms.
What is a relational database management system?
Relational database management systems help control data in scalable way. Relational databases are designed for managing large amounts of business-critical customer information. However, as data in a database grows and takes on more complexity, it becomes increasingly difficult to keep it all organized, accessible, and secure. This is when database management systems (DBMS) help by adding a layer of management tools for relational tables. Just like varying database structures, different management systems offer different levels of organization, scalability, and application. When administrators work with large volumes of structured and un-structured data received in real time, relational database management systems help them analyze and aggregate the data to find predefined relationships. Controlling data with an RDBMS creates the highest value to businesses since it makes data that's used across multiple applications or located in multiple locations more manageable.
An RDBMS uses software that provides a consistent interface between users and applications and the database, making navigation much simpler for data users. This is particularly effective when working with big data since the volume of data dictates such consistency for users joining queries. Choosing a DBMS depends on where your data resides, the type of architecture that’s used, and how you plan to scale.
What is a relational database model?
A relational database model is typically highly structured and understands SQL programming language. Many databases use a relational model since they’re designed to organize data and identify relationships between key data points, making it easy to sort and find information. Most relational models follow the traditional column- and row-based table structure, providing an efficient, intuitive, and flexible way to store structured data. The relational model also solves the problem of multiple arbitrary data structures in databases.
Relational database models can range from small, desktop systems to large cloud-based systems. They use a SQL database or they can process SQL statements for requests and updates. Relational models are defined by logical data structures (tables, indexes, and views) and are kept separate from physical storage structures (physical files). Data consistency is a hallmark of relational database models since they maintain data integrity across applications and database copies, also called instances. With a relational model database, multiple instances of a database always have the same data.
Relational databases designed in the cloud are automatically configured for high availability, meaning the data replicates or is copied on multiple members with each member sitting on separate availability zones. This way, data is still accessible, even if an individual data center is down.
Big data and relational databases
Traditional relational databases are built to handle large volumes of structured data. This makes relational databases particularly well-suited for big data that's structured since they rely on SQL and can make use of database management systems to control the data. However, big data's larger, more complex data sets increasingly contain more variety, which means data is getting less and less structured and it’s coming from new sources. This often calls for the use of nonrelational databases (or often called NoSQL databases), which can handle huge volumes of rapidly changing, unstructured data in different ways than a relational (SQL) database with rows and tables.