What is SQL? Full Form & Basics Tutorial

What is SQL?

SQL is the standard language for working with relational databases. It is used to insert, search, update, and delete records, and it can also optimize and maintain databases. SQL is an ANSI and ISO standard, so the same core syntax runs across engines such as PostgreSQL, MySQL, Oracle, and SQL Server.

SQL Full Form

SQL stands for Structured Query Language, pronounced as “S-Q-L” or “See-Quel”. IBM developed it in the 1970s, and relational databases such as MySQL, Oracle Database, Microsoft SQL Server, PostgreSQL, and SAP Sybase use ANSI SQL as their foundation.

How to Use SQL

You issue SQL by writing a statement that names the operation, the target table, and any filter conditions. A SELECT query returning members older than 30 looks like this:

SELECT * FROM Members WHERE Age > 30;

Syntax across databases is largely similar because every major engine implements the ANSI standard. A few RDBMS products add proprietary dialects, for example T-SQL in SQL Server and PL/SQL in Oracle.

Click here if the video is not accessible

What is SQL Used For?

Here are the important reasons for using SQL:

• It helps users access data in the RDBMS system.
• It helps you describe the data.
• It allows you to define the data in a database and manipulate that specific data.
• With the help of SQL, you can create and drop databases and tables.
• SQL lets you use functions in a database, create a view, and write stored procedures.
• You can set permissions on tables, procedures, and views.
• SQL supports analytical tasks such as reporting, business intelligence, and feeding data to machine learning pipelines.

A Brief History of SQL

Here are important landmarks from the history of SQL:

• 1970 – Dr. Edgar F. “Ted” Codd described a relational model for databases.
• 1974 – Structured Query Language appeared at IBM Research.
• 1978 – IBM released System/R.
• 1986 – ANSI standardized SQL as SQL-86.
• 1989 – First commercial version of the SQL standard was published.
• 1999 – SQL:1999 added triggers and object-oriented features.
• SQL:2003 – Window functions and XML features.
• SQL:2006 – Support for XML Query Language.
• SQL:2011 – Temporal database support.
• SQL:2016 – JSON, row pattern recognition, polymorphic table functions.
• SQL:2023 – Property graph queries (SQL/PGQ) and tighter JSON handling.

Types of SQL Statements

Here are five types of widely used SQL statements:

• Data Definition Language (DDL)
• Data Manipulation Language (DML)
• Data Control Language (DCL)
• Transaction Control Language (TCL)
• Data Query Language (DQL)

SQL Process

When you execute an SQL command, the database engine parses the statement, picks an execution plan, and runs it against the storage layer.

Important components in this process are:

• SQL Query Engine
• Optimization Engine
• Query Dispatcher
• Classic Query Engine

The classic query engine handles non-SQL queries, while the SQL query engine processes the structured workload.

SQL Standards

SQL covers database creation, deletion, fetching and modifying rows, and access control. It is an ANSI (American National Standards Institute) and ISO standard, and the current edition is ISO/IEC 9075:2023, commonly called SQL:2023. The standard is divided into several parts:

Modern SQL Engines

Each engine implements most of the ANSI core and adds its own extensions:

• PostgreSQL 17: Open-source with strict standards compliance and rich JSON support.
• MySQL 8: Popular with web apps; supports window functions, CTEs, and a document store mode.
• Microsoft SQL Server 2022: Enterprise engine with query intelligence and Azure Synapse mirroring.
• Oracle Database 23ai: Combines SQL with vector search and JSON-relational duality views.
• SQLite: Lightweight file-based engine embedded in mobile apps and browsers.

Cloud warehouses such as Amazon Redshift, Google BigQuery, and Snowflake also expose ANSI SQL dialects, making the language portable from a laptop to petabyte-scale analytics.

SQL Language Elements

Here are the important elements of the SQL language:

• Keywords: Reserved words such as SELECT, FROM, or WHERE that build every statement.
• Identifiers: Names of database objects such as user IDs, tables, and columns.
• Strings: Literal strings or expressions with VARCHAR or CHAR data types.
• Expressions: Formed from constants, SQL operators, column names, and subqueries.
• Search Conditions: Filter rows or control flow statements like IF.
• Special Values: Values such as CURRENT_TIMESTAMP used in expressions and column defaults.
• Variables: Most engines support local, global, and connection-level variables in stored procedures.
• Comments: Single-line (–) and block (/* */) comments document SQL but are not executed.
• NULL Value: NULL marks a value that is unknown, missing, or not applicable.

What is a Database in SQL?

A database is a collection of tables that store structured data. A table contains rows (records or tuples) and columns (attributes).

Each column stores a specific type of information, for example names, dates, dollar amounts, or numbers. Relationships between tables are enforced with primary and foreign keys, which is what makes the database “relational”.

What is NoSQL?

NoSQL is a category of Database Management Systems whose main trait is non-adherence to relational concepts. NoSQL means “Not only SQL”. The category grew with internet giants such as Google, Facebook, and Amazon, who deal with gigantic data volumes.

When relational databases handle massive volumes, response times can slow. You can “scale up” by upgrading hardware, or “scale out” by distributing the load across multiple hosts.

NoSQL databases are non-relational stores designed for web applications. They do not always use SQL or enforce strict schemas, and ACID (Atomicity, Consistency, Isolation, Durability) properties are not always guaranteed, although modern systems such as MongoDB and Cassandra now offer tunable transactional modes.

Why It Makes Sense to Learn SQL After NoSQL

Given that NoSQL scales out better than relational models, you might wonder why someone would still learn SQL. NoSQL databases are specialized and best for huge unstructured datasets, but most business applications still rely on relational databases and their tooling.

Relational databases retain the following advantages over NoSQL:

• Mature data storage and management model, crucial for enterprise users.
• Support for views, letting users see only authorized data.
• Support for stored procedures that push business logic into the database.
• Stronger security models, role-based access controls, and audit features.
• SQL is the lingua franca of BI, dashboards, and AI tools that need structured input.

Demand for professionals who can handle relational databases keeps growing, so learning SQL basics still holds clear merit.

SQL and AI: Text-to-SQL Tools

SQL is now a target language for AI assistants. Text-to-SQL tools translate plain English questions, such as “show last quarter’s top 10 customers by revenue”, into executable SQL. Popular options include GitHub Copilot, Vanna AI, Microsoft Copilot in Azure SQL, and Oracle Select AI. Always review generated SQL for correctness, security, and indexing before running it on production data.

The Best Books to Learn SQL

Here are some of the top SQL books:

• SQL Tutorial for Beginners:

  This SQL Tutorial for beginners PDF covers basic database concepts, MS-SQL commands, and advanced topics such as SQL joins.

• SQL in 10 Minutes: Full-color code examples show how SQL statements are structured, plus shortcuts and ready-made solutions. BUY NOW
• SQL Cookbook: Techniques such as walking a string with SQL to parse characters or delimited elements. BUY NOW
• SQL: The Complete Reference: Covers window functions, pivoting rows into columns, and reverse-pivoting. BUY NOW
• SQL Pocket Guide: Teaches SQL functions, regular expression syntax, and type conversion. BUY NOW

For more SQL books – Click here

FAQs