What is a SQL database?
SQL database is a data collection visualized as tables with rows and columns. Data is stored similarly to a spreadsheet, with columns indicating data attributes and rows describing the entity or object the data is about. Most SQL databases use the structured query language (SQL) for user-data interaction—hence the name. They are relational databases because you can store data relationships between tables.
For example, a products table has columns like product name, type, cost, etc, and a row contains values for the individual products. A customer table has columns with customer names and contact details. You can create a third table that links customer data with the products they purchased.
What are the benefits of SQL databases?
SQL databases are widely used across various industries because they are logically intuitive to implement and work with.
Efficient data management
SQL databases offer well-defined schemas and relational tables so you can organize data predictably. Constraints enforce rules on the data entered, resulting in accurate data retrieval. A set of four properties—atomicity, consistency, isolation, and durability (ACID)—ensure data integrity.
- Atomicity guarantees that all parts of an operation sequence(transaction) are completed or none at all.
- Consistency ensures that transactions bring the database from one valid state to another.
- Isolation prevents transactions from interfering with each other.
- Durability ensures that once a transaction is committed, it is permanently saved, even in a system failure.
You can ensure that all sequential database operations are completed successfully or rolled back if an error occurs.
High performance
SQL databases can handle large data volumes and scale to meet business needs. They offer various indexing and optimization techniques to ensure fast query performance even as the data size increases. Features like replication, clustering, and failover mechanisms ensure high availability. These features help maintain database uptime and protect against data loss during unexpected outages.
Ease of use
SQL is a standardized language used across various database systems with minimal changes. It allows for easy migration between different SQL-based systems. SQL databases are also compatible with various data tools, including business intelligence (BI) platforms, analytics software, and reporting tools. You can access extensive resources, tools, and support from a wide and active community.
How does a SQL database work?
A SQL database, or relational database, works with an underlying storage engine. The storage engine is responsible for the physical data storage on the disk. Different SQL databases use different storage engines. For example, MySQL uses InnoDB and MyISAM. The storage engine abstracts your mental data model so you can work with data instead of worrying about its physical storage. The choice of storage engine affects performance, transaction handling, and other database features.
Schema
A schema defines the structure of the database, including the tables, their relationships, and constraints. It acts as a blueprint for how data is stored and organized. The table in the SQL database consists of rows (records) and columns (fields). Each column has a specific data type (e.g., integer, text, date) and stores particular information. Each row represents a single record.
SQL language
SQL is the structured query language used to interact with all relational database management systems. Commands like select, insert, update, and delete manage data within tables. Commands like create, alter, and drop define or modify the database structure.
Other mechanisms
The storage engine uses different mechanisms to optimize database performance. For example, indexes are special lookup tables the storage engine uses to speed up data retrieval. An index is created on a column or a set of columns and helps locate data quickly without searching every row in a table.
What are some examples of SQL database technology?
Many different relational database systems implement the SQL database. We give some examples below, but it is not a comprehensive list.
MySQL
MySQL is commonly used in web applications, particularly as part of the Linux, Apache, MySQL, and PHP LAMP stack. It offers replication, partitioning, and various storage engines to optimize workloads.
PostgreSQL
PostgreSQL is an open-source object-relational database system that extends the SQL language with additional features, including support for JSON, XML, and custom data types. It is often used in applications that require large-scale data management. PostgreSQL supports advanced features like full-text search, indexing, and transactional integrity.
MariaDB
MariaDB is a fork of MySQL created by the original developers after Oracle acquired it. It has introduced several improvements, including new storage engines and additional features like JSON support, dynamic columns, and thread pooling. It is used in various enterprise-level applications for its stability and scalability.
Microsoft SQL Server
Microsoft SQL Server is a relational database management system developed by Microsoft. It offers in-memory processing, advanced analytics, and high availability through Always On availability groups.
Oracle database
Oracle database is a multi-model database management system developed by Oracle Corporation. It is known for its scalability, performance, and security features. Oracle supports many data models, including relational, document, graph, and key-value stores. It offers features like Real Application Clusters (RAC), Automatic Storage Management (ASM), and data security options.
IBM Db2
IBM Db2 is known for its high performance, advanced analytics capabilities, and support for large-scale enterprise applications. It offers in-memory processing, AI integration, and advanced security options.
What is the difference between a NoSQL and SQL database?
NoSQL databases are schema-less and don't follow the strict table structures that the SQL database implements. You typically store data as key-value pairs in documents. They are more suitable for unstructured data like social media posts, emails, etc., which are inefficient to store as tables. The SQL database is relational, but the NoSQL database is a non-relational database management system.
Scalability
NoSQL databases are designed for distributed computing. You can scale out by adding more servers or nodes to distribute the load across multiple machines. The modern SQL database lets you do the same but can often result in performance trade-offs.
Flexibility
SQL databases require predefined schemas, meaning the data structure must be known upfront, and changes to the schema can be complex. In contrast, NoSQL databases allow dynamic and evolving data structures, making them suitable for applications with changing or unpredictable data requirements. They also use more flexible query languages customized to their specific data models. However, most are compatible with SQL to some extent.
Consistency
SQL databases prioritize data consistency and reliability. Depending on the use case, NoSQL databases sacrifice some consistency in favor of higher availability and partition tolerance.
Use cases
SQL databases are suitable for applications requiring complex SQL queries, transactions, and structured data, such as financial systems and enterprise resource planning (ERP). In contrast, NoSQL databases excel in scenarios involving large volumes of unstructured or semi-structured data, high scalability needs, and rapid development.
How can AWS support your SQL database requirements?
Amazon Relational Database Service (Amazon RDS) is a collection of managed services that makes it simple to set up, operate, and scale a SQL database in the cloud. You can run RDS with a SQL database engine of your choice.
- Amazon Aurora combines the speed and availability of high-end relational databases with the simplicity and cost-effectiveness of open-source databases.
- Amazon RDS for Oracle allows you to deploy multiple editions of Oracle Database in minutes with cost-efficient and resizable hardware capacity.
- Amazon RDS for SQL Server makes it easy to set up, operate, and scale SQL Server in the cloud.
- Amazon RDS for MySQL gives you access to the capabilities of a familiar MySQL database engine.
- Amazon RDS for PostgreSQL manages undifferentiated, time-consuming database administrative tasks so you can deploy and get started within minutes.
- Amazon RDS for MariaDB makes setting up, operating, and scaling MariaDB deployments in the cloud.
- Amazon RDS for Db2 lets you set up, operate, and scale a Db2 database in the cloud with just a few clicks.
Get started with your SQL database on AWS by creating a free account today!
