Structured query language (SQL) is a programming language for storing and processing information in a relational database. A relational database stores information in tabular form, with rows and columns representing different data attributes and the various relationships between the data values. You can use SQL statements to store, update, remove, search, and retrieve information from the database. You can also use SQL to maintain and optimize database performance.
Why is SQL important?
SQL is essential in the data industry and is used by analysts, scientists and database developers. Its universality makes it a valuable skill in understanding other programming languages like Python and Java. Since its development in the 1970s, SQL has become the backbone of modern database systems.
SQL is used for storing, retrieving, managing and manipulating data within database management systems (DBMS). It supports transaction processing and analytics applications and integrates seamlessly with various programming languages, such as Java, allowing developers to build high-performance data processing applications for different business intelligence needs.
Basic operations include these SQL commands:
- SELECT * FROM customers ; to retrieve all records from the ‘customers’ table.
- INSERT INTO customers (name, email) VALUES (‘John Doe’, ‘john@example.com’) ; to add a new customer or UPDATE customers
- SET email = ‘newemail@example.com’ WHERE name = ‘John Doe’ ; to update an email address.
SQL operations are conducted through commands written as statements. These statements are aggregated into programs that facilitate the addition, modification or retrieval of data from a database, which consists of rows and columns in tables. Each row holds a record and each column represents a related category of data. In addition to tables, other database objects include views (logical data representations), indexes (to speed up lookups) and reports (data subsets based on criteria).
SQL databases are designed to help ensure reliable transaction processing by adhering to ACID properties: Atomicity (all-or-nothing), Consistency (protects database integrity), Isolation (transactions do not interfere with each other) and Durability (helps ensure that changes from successful transactions persist even if the system fails).
Relational databases organize data by using these rows and columns to structure information, such as customer information and purchase details. These tables are linked through relationships, often by using a specific identifier or primary key to facilitate efficient data retrieval and management.
While standard SQL is widely used, specific dialects like T-SQL by Microsoft SQL Server and PL/SQL by Oracle offer extensions to enhance procedural programming capabilities. SQL is known for its create, read, update and delete functions and its nonprocedural nature makes it accessible even to those with limited programming experience, often making it an easy language for beginners. It adheres to ANSI standards and is available in free SQL software like MySQL, PostgreSQL and Microsoft SQL.
What are the components of a SQL system?
Relational database management systems use structured query language (SQL) to store and manage data. The system stores multiple database tables that relate to each other. MS SQL Server, MySQL, or MS Access are examples of relational database management systems. The following are the components of such a system.
SQL table
A SQL table is the basic element of a relational database. The SQL database table consists of rows and columns. Database engineers create relationships between multiple database tables to optimize data storage space.
For example, the database engineer creates a SQL table for products in a store:
| Product ID | Product Name | Color ID |
| 0001 | Mattress | Color 1 |
| 0002 | Pillow | Color 2 |
Then the database engineer links the product table to the color table with the Color ID:
| Color ID | Color Name |
| Color 1 | Blue |
| Color 2 | Red |
SQL statements
SQL statements, or SQL queries, are valid instructions that relational database management systems understand. Software developers build SQL statements by using different SQL language elements. SQL language elements are components such as identifiers, variables, and search conditions that form a correct SQL statement.
For example, the following SQL statement uses a SQL INSERT command to store Mattress Brand A, priced $499, into a table named Mattress_table, with column names brand_name and cost:
INSERT INTO Mattress_table (brand_name, cost)
VALUES(‘A’,’499’);
Stored procedures
Stored procedures are a collection of one or more SQL statements stored in the relational database. Software developers use stored procedures to improve efficiency and performance. For example, they can create a stored procedure for updating sales tables instead of writing the same SQL statement in different applications.
What are SQL standards?
SQL standards are a set of formally defined guidelines of the structured query language (SQL). The American National Standards Institute (ANSI) and International Organization for Standardization (ISO) adopted the SQL standards in 1986. Software vendors use the ANSI SQL standards to build SQL database software for developers.
What is SQL injection?
SQL injection is a cyberattack that involves tricking the database with SQL queries. Hackers use SQL injection to retrieve, modify, or corrupt data in a SQL database. For example, they might fill in a SQL query instead of a person’s name in a submission form to carry out a SQL injection attack.
What is MySQL?
MySQL is an open-source relational database management system offered by Oracle. Developers can download and use MySQL without paying a licensing fee. They can install MySQL on different operating systems or cloud servers. MySQL is a popular database system for web applications.
SQL vs. MySQL
Structured query language (SQL) is a standard language for database creation and manipulation. MySQL is a relational database program that uses SQL queries. While SQL commands are defined by international standards, the MySQL software undergoes continual upgrades and improvements.
What is NoSQL?
NoSQL refers to non-relational databases that don’t use tables to store data. Developers store information in different types of NoSQL databases, including graphs, documents, and key-values. NoSQL databases are popular for modern applications because they are horizontally scalable. Horizontal scaling means increasing the processing power by adding more computers that run NoSQL software.
SQL vs. NoSQL
Structured query language (SQL) provides a uniform data manipulation language, but NoSQL implementation is dependent on different technologies. Developers use SQL for transactional and analytical applications, whereas NoSQL is suitable for responsive, heavy-usage applications.
What is a SQL server?
SQL Server is the official name of Microsoft’s relational database management system that manipulates data with SQL. The MS SQL Server has several editions, and each is designed for specific workloads and requirements.
What’s the difference between SQL and NoSQL databases?
SQL databases organize data in columns and rows. The rows contain data sets that apply to each item, while the columns define the different properties of those items, such as product ID, name, quantity, and unit price. In contrast, NoSQL databases store all incoming data in a single document with no overt structure. This makes SQL databases conducive to storing structured forms of data while NoSQL databases are equipped to handle large volumes of structured, semi-structured, and unstructured data from non-traditional sources.
Below, you will find key differences between SQL and NoSQL databases.
| Features | SQL | No SQL |
| Types of data | Structured | Structured, semi-structured, unstructured |
| Types of databases | Relational | Relational and non-relational |
| Structure or schema | Tables | Document |
| Data format | Processed | Raw |
| Flexibility | Low | High |
| Availability | High | Low |
| Sources | Applications, transactional data, batch reporting, BI | Big data, IoT, social media, streaming data |
Benefits of using SQL databases
Now you know what an SQL database is and how it functionally differs from a NoSQL database. But why do organisations use relational databases, and in what situations do they use them? SQL databases offer countless benefits that make data management easy, including:
- Optimized performance. Due to high processing speeds and minimal storage usage, relational databases can retrieve enormous batches of data transactions in near-real time, then insert, delete, or modify that data almost instantly.
- Seamless collaboration. With an SQL database, you have the ability modify the schema in real time. This means any user can add, remove, and modify the data inside each column and row, or even remove the columns and rows themselves, all without disrupting the workflow of other users.
- Clean, reliable data. SQL databases not only maintain consistency across all server instances, but they also reduce redundancies in the data related to insertions and deletions—which is essential for maintaining accuracy and speed when processing a large batch of transactions.
- Easy to access and learn. SQL is written in plain English and not in complex code, which makes it easier for the average user to select, insert, update, and delete data, so long as they learn the functions and the syntax.
- Community support. Because SQL is an open-source language, it is supported by a worldwide community of developers who provide continuous updates, documentation, and troubleshooting help when the need arises.
Common use cases for SQL databases
In a data-centric world, SQL databases run the show. Software companies, banks, government agencies, universities, and organizations from different industries rely on relational databases to store records, manage data, and perform important business operations. Here are just a few use cases out of many:
- Mission-critical applications. Financial systems, payment processors, and other mission-critical apps that are working with highly structured, centralized data use SQL technology to support transactions—and provide an added layer of security along the way.
- Business intelligence. Across a wide range of industries, BI analysts use relational databases to pull reports and make them available to operations, HR, sales, warehousing, and marketing departments for further analysis and decision-making.
- Data integrity. Apps that work with private, sensitive data sets rely on SQL technology to set well-defined constraints, avoid data duplication, and establish data integrity. This ensures that the data not only remains reliable and secure, but also complies with standards of atomicity, consistency, isolation, and durability (ACID), also known as ACID compliance.
- Other types of apps. Apps that handle high volumes of structured data sets, but aren’t always required to be on, rely on SQL databases to support periodic scaling and growth.
Types of databases
Now that you know the benefits and applications of SQL databases, let’s get an overview of the different types and how they differ from each other.
First, you’ll want to determine when to choose a relational database over a nonrelational database and vice-versa. Because SQL databases are structured, they are perfect for handling transactions, records, and libraries. NoSQL databases have the capacity to support all forms of data, including texts, images, IoT notifications, and other forms of big data. Both SQL and NoSQL databases make their respective forms of data readily available for search and analysis.
Let’s look at some commonly used SQL and NoSQL databases:
- SQL Server. SQL databases services are typically built on a relational database management system—and many enjoy the capabilities of cloud computing. Azure SQL, for instance, is a family of managed SQL database services built on the SQL Server database engine and hosted in the Azure cloud.
- PostgreSQL. PostgreSQL is an advanced object-relational database management system (ORDBMS) that stores and scales workloads that are too complex to be supported by your standard systems. A cloud-based service for PostgreSQL, like Azure Database for PostgreSQL, offers additional enterprise features and extensibility, as well as the support of PostgreSQL’s open-source community.
- MySQL. MySQL is one of the most popular, widely used open-source relational database management systems in modern app development. Unlike SQL, MySQL is a piece of software, not a language. A managed MySQL database service, like Azure Database for MySQL, lets you provision a database in minutes and scale just as quickly.
- NoSQL. NoSQL database services enable users to develop modern, responsive, high-performance apps at any size or scale. They also support a wide variety of open-source databases. Azure Cosmos DB supports PostgreSQL, MongoDB, and Apache Cassandra, is fully managed, auto-updates, and auto-scales.
The future of SQL
The future of SQL is in the cloud, which is why many organizations are now opting for cloud databases over traditional, on-premises solutions. A cloud database is a database service that’s been built and hosted on a cloud platform, providing the same functionality as an on-premises server, but with the infrastructure and scalability of cloud computing.
Here’s how cloud databases compare to on-prem:
- Managed services. A fully managed cloud database-as-a-service streamlines and automates updates, backups, provisioning, and other configuration tasks so that you can focus on developing high-performance apps.
- No hardware required. Organizations can now enjoy the benefits of an SQL database without having to buy the dedicated server hardware to support it. And by hosting in the cloud, your database will always update to the latest version of SQL.
- More flexibility. While most on-premises SQL databases can only support relational databases like MySQL and PostgreSQL, cloud databases can support SQL databases and NoSQL databases, including MongoDB and Apache CouchDB.
- Easier access. SQL databases are generally easy to access, but users now have the ability to access a cloud database with even more ease through a vendor API or web interface.
- Cost-saving scalability. Cloud databases can scale up or down depending on an organization’s usage requirements, allowing them to only use the resources they need, and to only pay for what they use.
- Disaster recovery. With the help of remote server backups, cloud databases remain available and secure, even when disaster strikes, and the power goes out.