Structured Query Language (SQL)

Reviewed by: Richard Becker
Last Updated: August 14, 2020

Definition - What does Structured Query Language (SQL) mean?

Structured Query Language (SQL) is a programming language that is typically used in relational database or data stream management systems.

It was developed by IBM in the early 1970s and is now an official standard recognized by the American National Standards Institute (ANSI) and the International Organization for Standardization (ISO).

Techopedia explains Structured Query Language (SQL)

SQL has remained a consistently popular choice for database users over the years primarily due to its ease of use and the highly effective manner in which it queries, manipulates, aggregates data and performs a wide range of other functions to turn massive collections of structured data into usable information.

For this reason, it has been incorporated into numerous commercial database products, such as MySQL, Oracle, Sybase, SQL Server, Postgres and others. In fact, many non-relational databases like MongoDB and DynamoBD are called NoSQL products due to their lack of SQL programming.

While different iterations of SQL may utilize different syntax for key operations, in general, basic commands like select, insert, update and create are common to all SQL releases. This makes it very easy for someone with a basic knowledge of SQL to work in many different environments and perform a wide variety of tasks.

Three Categories of SQL Commands

Data Definition Language (DDL)

This includes CREATE (tables, views, objects, etc.), ALTER and DROP (delete).

Data Manipulation Language (DML)

SELECT, INSERT, UPDATE, DELETE of records within tables.

Data Control Language (DCL)

GRANT and/or REVOKE user privileges, etc.

Streamlining Search and Other Functions

SQL also allows users to build constraints onto tables or columns to restrict the type of data they contain. This helps ensure data accuracy and relevancy, and simplifies overall database management by streamlining search and other functions.

Some examples of SQL constraints include:


Which prevents columns from having a null value.


To ensure all values are different.

In addition, DBAs can use SQL to build integrity into the database by preventing the creation of duplicate rows, allowing only the entry of valid data, forbidding deletion of data tied to multiple records, and other functions.

At the same time, however, SQL provides a number of normalization tools designed to streamline data dependencies and in general reduce the size and scope of the database to make it operationally effective and resource efficient.

Obviously, SQL is not the best choice for all database applications, otherwise there would be no alternatives. For one thing, while SQL had been effective at data scales up through the 1990s and beyond, the implementation and relational database management systems (rather than the language itself) started to falter at the hyperscale levels at the turn of the century.

Some users also complain of its sharding limitations, which hamper the ability to break large databases into smaller, more manageable ones.

These drawbacks, in fact, are what led to the creation of NoSQL and the more recent NewSQL, which attempts to enhance traditional SQL’s scalability without sacrificing its inherent atomicity, consistency, isolation and durability (ACID).

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