SQL is a bit different from the other languages. It's formally pronounced “ess cue el,” although some people say “sequel.” While the acronym stands for “Structured Query Language,” many people do not consider SQL to be like other software development languages because it's a non-procedural language and its scope is limited to querying and managing data. While it is not a “data science” language, data scientists regularly use it because it's simple and powerful!
Another couple of neat facts
about SQL: it's much older than Python and R, by about 20 years, having first
appeared in 1974. And, SQL was developed at IBM! This language is useful in
handling structured data; that is, the data incorporating relations among
entities and variables. SQL was designed for managing data in relational databases.
SQL was originally developed for
use with relational databases because it's so pervasive and easy to use, SQL
interfaces for many NoSQL and big data repositories have also been developed.
The SQL language is subdivided
into several language elements, including clauses, expressions, predicates,
queries, and statements.
So
what makes SQL great?
Knowing SQL will help you do many
different jobs in data science, including business and data analysis, and it's a
must in data engineering. When performing operations with SQL, you access the
data directly. There's no need to copy it beforehand. This can speed up
workflow executions considerably. SQL is the interpreter between you and the database.
There are many different SQL
databases available, including MySQL, IBM Db2, PostgreSQL, Apache OpenOffice
Base, SQLite, Oracle, MariaDB, Microsoft SQL Server, and more.
