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What is Structured Query Language (SQL)?

By   /  August 21, 2017  /  No Comments

SQLSQL or Structured Query Language, is a programming nomenclature used to do set operations (like union, intersect, and minus) to organize and retrieve relational databases, an object based on “set theory and relational algebra.” In any system that uses SQL, “data elements or attributes, categorized into columns, are related into tuples (rows). Sets of relations with identical structure form tables.” These elements, correlated rows, columns and tables form the basis for a Relational Database Management System (RDBMS).

SQL’s power comes from its conformity. According to the American National Standards Institute (ANSI), SQL is a data sub-language widely used to access relational databases. This means knowing and using SQL from RDBMS will be generalizable to another RDBMS, resulting in efficient querying and reporting. Also, SQL can be picked up easily through accessible training and resources.

Other Definitions of SQL Include:

  • A “relational data language that provides a consistent, English keyword-oriented set of facilities for query, data definition, data manipulation, and data control.”  (Gartner)
  • “The standard language for RDMS for the last 40 years. (Beth Narrish and Dan Hilton)
  • “A standard interface for RDBMS.” (Andrew Pavlo and Matthew Aslett)
  • “Language used to manage and administer the database server.” (Database Journal)
  • “Most common language for querying and manipulating data.” (Angela Zhang, Forbes)
  • “A query language designed for organizing, managing developing and querying large relational databases over computer networks.” (IBM)
  • “A specialized language for updating, deleting and requesting information from databases.” (Indiana University)

Businesses Use SQL to:

  • Access and manipulate information.
  • Generate reports to aid decision making process.
  • Make it easier to import and export data to/from different systems.
  • To facilitate finding and supporting roles using Data Analysis.
  • Provide strict ACID constraints to data assets.


Photo Credit: chrupka /Shutterstock.com

About the author

Michelle Knight enjoys putting her information specialist background to use by writing technical articles on enhancing Data Quality, lending to useful information. Michelle has written articles on W3C validator for SiteProNews, SEO competitive analysis for the SLA (Special Libraries Association), Search Engine alternatives to Google, for the Business Information Alert, and Introductions on the Semantic Web, HTML 5, and Agile, Seabourne INC LLC, through AboutUs.com. She has worked as a software tester, a researcher, and a librarian. She has over five years of experience, contracting as a quality assurance engineer at a variety of organizations including Intel, Cigna, and Umpqua Bank. During that time Michelle used HTML, XML, and SQL to verify software behavior through databases Michelle graduated, from Simmons College, with a Masters in Library and Information with an Outstanding Information Science Student Award from the ASIST (The American Society for Information Science and Technology) and has a Bachelor of Arts in Psychology from Smith College. Michelle has a talent for digging into data, a natural eye for detail, and an abounding curiosity about finding and using data effectively.

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