Data Query Language

 

DATA QUERY LANGUAGE(DQL)

Introduction

Data Query Language (DQL) is a set of commands used to retrieve, filter, and manipulate data from databases. It allows users to query databases and extract specific information based on their requirements.

What is DQL?

Data Query Language (DQL) is one of the basic sub-languages of SQL statements. There are generally four categories in SQL languages which are data query language (DQL), data definition language (DDL), data control language (DCL), and data manipulation language(DML). It is also occasionally suggested that a transaction control language (TCL) belongs in the sub-language set.

DQL statements are employed to conduct inquiries on the information contained in schema objects. The required data is retrieved according to the query using Data Query Language (DQL) commands.

Data Query Language (DQL) refers to a language or set of commands used to query databases and retrieve specific data. DQL is typically associated with database management systems (DBMS) and provides a way to interact with databases to perform various operations. Here are some common uses of DQL:

1.     Data Retrieval: The primary use of DQL is to retrieve data from a database. By using DQL commands such as SELECT, users can specify the criteria and conditions to retrieve specific data records or fields. DQL allows you to query databases to obtain the information you need for analysis, reporting, or application development.

2.     Filtering and Sorting: DQL enables filtering and sorting of data based on specific conditions. You can use DQL commands like WHERE to specify conditions that filter data based on certain criteria, such as dates, values, or text patterns. DQL also supports sorting data using ORDER BY, allowing you to arrange query results in ascending or descending order based on one or more columns.

3.     Aggregation and Grouping: DQL provides functionality for aggregating and summarizing data. You can use aggregate functions like COUNT, SUM, AVG, MAX, and MIN to perform calculations on groups of data. DQL also allows you to group data based on one or more columns using the GROUP BY clause, enabling the creation of summary reports or analysis based on different categories.

4.     Joining Multiple Tables: DQL allows you to combine data from multiple tables using join operations. By specifying join conditions, you can merge data from related tables into a single result set. This is particularly useful when you need to extract data that is distributed across different tables and establish relationships between them.

5.     Data Modification: Although the primary focus of DQL is querying and retrieving data, some database systems also provide DQL commands to modify data. For example, you can use DQL commands like INSERT, UPDATE, and DELETE to add, modify, or remove data records in a database.

6.     Data Definition: DQL can also be used for defining and managing the structure of databases. DQL commands like CREATE TABLE, ALTER TABLE, and DROP TABLE are used to create, modify, and delete database objects such as tables, indexes, and constraints.

7.     Database Administration: DQL is also utilized for database administration tasks. Database administrators use DQL to manage user permissions and access privileges, create and maintain database schemas, optimize query performance, and perform other administrative functions necessary for the efficient operation of a database system.

Conclusion

Thus this is how Data Query Language (DQL) is used.

Data Bending

Databending is the process of manipulating a media file of a certain format, using software designed to edit files of another format. Distortions in the medium typically occur as a result, and the process is frequently employed in glitch art.

Reference

1.      https://www.javatpoint.com/data-query-language

2.      https://en.wikipedia.org/wiki/Databending

Hitansh Lakkad

Business Analytics intern at Hunnarvi Technologies Pvt Ltd in collaboration with nanobi analytics.

VIEWS ARE PERSONAL

#Bigdata#analytics#datastorage#datamanagement#datastorage#DQL#databending

 

 

 

 

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