What is the Difference Between DDL and DML

The main difference between DDL and DML is that DDL helps to change the structure of the database while DML helps to manage the data in the database.

A database is a collection of related data. DBMS (Database Management System) is a software that helps to access and manage databases easily. They allow allows multiple users to access data and provide security to data. Relational DBMS stores data in table format. A table consists of rows and columns. Rows represent each record while DML columns represent attributes. SQL (Structured Query Language) is the language to store, manage and retrieve data in Relational databases. The user can perform various operations on the database using SQL commands. Two types of SQL commands are DDL and DML.

Key Areas Covered

1. What is DDL
     – Definition, Functionality
2. What is DML
     – Definition, Functionality
3. What is the Difference Between DDL and DML
     – Comparison of Key Differences

Key Terms

DBMS, DDL, DML, SQL

Difference Between DDL and DML - Comparison Summary

What is DDL

DDL stands for Data Definition Language. DDL commands help to create the structure of the database and the other database objects. Its commands are auto-committed. Therefore, the changes are saved in the database permanently.

Some DDL commands include create, drop and alter. The create command helps to create new databases and tables. The drop command helps to delete databases and tables while the alter command helps to modify an already existing database object such as a table. Those are some common DDL commands.

What is DML

DML stands for Data Manipulation Language. DML commands help to manage the data stored in the database. However, DML commands are not auto-committed. Therefore, changes are not permanent. Hence, it is possible to roll back the operation.

Some DML commands include insert, update, delete and select. The insert command helps to store new records or rows to the table while the update command helps to modify an existing record in the table. Meanwhile, the delete command allows deleting a certain record or a set of records from the table while the select command allows retrieving specific records from one or more tables.

Difference Between DDL and DML

Figure 1: SQL Commands

In addition to DDL and DML, there is another command type as DCL. DCL allows controlling the users who can access the databases. Grant and revoke are two DCL commands. Grant gives privilege to a user whereas revoke takes back the privilege granted from the user.

Difference Between DDL and DML

Definition

DDL is a type of SQL command that helps to define database schemas. In contrast, DML is a type of SQL command that helps to retrieve and manage data in relational databases. These definitions explain the main difference between DDL and DML.

Long Form

DDL stands for Data Definition Language while DML stands for Data Manipulation Language.

Commands

Create, drop, alter are some DDL commands while insert, update, delete and select are some DML commands.

Effect

Another difference between DDL and DML is that DDL commands affect the entire database or the table, but DML commands affect one or more records in a table.

Commit

SQL statements with DDL commands cannot be rolled back. On the other hand, SQL statements with DML commands can be rolled back. Hence, this adds to another difference between DDL and DML.

Conclusion

SQL is the language that helps to perform various operations on data in relational databases. DDL and DML are two types of SQL commands. The main difference between DDL and DML is that DDL helps to change the structure of the database while DML helps to manage the data in the database.

Reference:

1. “SQL Overview.” Www.tutorialspoint.com, Available here.
2. “Introduction to SQL.” Introduction to SQL (Structure Query Language) | Studytonight, Available here.

About the Author: Lithmee

Lithmee holds a Bachelor of Science degree in Computer Systems Engineering and is reading for her Master’s degree in Computer Science. She is passionate about sharing her knowldge in the areas of programming, data science, and computer systems.

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