What Is SQL?

SQL allows programmers to query, update and reorganize data while modifying database schema to control data accessibility.

Written by Anthony Corbo
Published on Dec. 29, 2022
What Is SQL?
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Eric Kleppen | Aug 11, 2022

SQL, or Structured Query Language, is a domain-specific language that’s been in use since the 1970s. It’s still widely used in analytics, data engineering and data science to help manage data stored in a relational database system and can be vital when working with structured data. SQL allows programmers to query, update and reorganize data as needed while also modifying database schema to control data accessibility. MySQL and PostgreSQL are two of the most popular open-source relational database management systems while some common enterprise-level systems that utilize SQL include Oracle, Sybase, Microsoft SQL Server, Access, Ingress and many other proprietary extensions.

Who Uses SQL?

  • Data analysts rely on SQL to quickly pull and aggregate data from a database.
  • Data engineers rely on SQL to move volumes of data through various systems.
  • Data scientists rely on SQL to quickly analyze and clean data before using it in complex modeling.


How Does SQL Work?

SQL is considered to be the most important tool at the disposal of anyone who works with data, from data scientists to engineers and statisticians as it is used to access, query, update and reorganize data within a database.

At its core, a database is simply a system for organizing data stored on disk. Relational databases work by storing structured data within tables. These tables make up the database schema, and oftentimes, these tables will be linked together through one or more common fields. These relationships between common fields allow users to access data from separate tables simultaneously upon user query. This process is called joining tables.

The tables and data within databases are stored in separate files on disk for easy access and permanent storage compared to data stored in memory. Understanding the database’s file system is an important part of architecting data intensive applications, but is not a requirement for learning and using SQL. 

For relational databases that use SQL, an SQL engine and query optimizer are used in conjunction with the file system. Together, they interpret and execute the SQL commands. The query engine transforms the SQL into something the computer can understand while the query optimizer finds the optimal way to retrieve the data from the files. The foundations of SQL and query optimization stem from relational algebra, but SQL is easy to learn and apply without knowing the theory upon which it was built.

SQL remains popular due to its relative ease of use for beginners. Standard SQL commands, such as select, insert, update, delete, create and drop can be used across all databases to accomplish virtually any task in SQL.

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What Is SQL Used For?

Generally, we use SQL to communicate with a database. Every company stores data to some extent, which means having the skills to access and manipulate data can result in a high-paying career.

SQL is useful when it comes to managing structured data in an organized manner, thereby making it easy to quickly access and arrange data.

SQL is particularly useful due to its ability to share and manage data found in a relational database management system, which allows users to query, update and reorganize key data, while also modifying database schema and determining how data can be accessed.

Due to these capabilities and use cases, SQL has become the most widely implemented programming language despite being created in the early 1970s.

What Is SQL? | Video: Danielle Thé


Should I Learn SQL or Python?

Oftentimes, working with structured data requires fetching the data in SQL and manipulating it in Python, so the short answer: Learn both if you can.

SQL is excellent for querying, extraction and merging tables from databases together to provide users with robust but specific datasets. SQL is not, however, the best choice when it comes to transforming data into other formats, which is often utilized in the form of regression testing and time-series data manipulation in data science. Python is the better option for tasks of this nature, particularly through its Pandas library, which is tailor-made for data manipulation and analysis.

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