DDL vs. DML: What’s the Difference?

The two components, DML (Data Manipulation Language) and DDL (Data Definition Language), play a critical role in manipulating data in database management systems. As we compare DDL and DML, you will understand what makes them distinct if you want to work with a database effectively.

DDL deals with the structural aspects of the database. It enables users to modify and define indexes, tables, constraints, schemas, and other database objects. In addition, we use DDL statements to alter or create drop-down database objects for the entire structure and organization of the data. In contrast, DML manipulates data within the database. Such operations include updating, deleting, inserting, and retrieving table data. When you use the DML statements, you will interact with stored data within the database, query information, make changes, and manage the database content. The information in this guide delves deeper into the differences between DML and DDL while exploring their syntax, specific functionalities, and use cases. 

DDL vs. DML: Side-by-Side Comparison

Features	DDL	DML
Stands for	Data Definition Language	Data Manipulation Language
Commands	The commonly used commands include RENAME, TRUNCATE, ALTER, DROP, and CREATE	The widely used commands include SELECT, DELETE, UPDATE, INSERT
Rollback	One cannot roll back statements as DDL commands make changes permanently	Rollback is possible, as the DML statements do not make changes permanent.
Impact	The DDL commands affect the entire table or database	The DML commands affect multiple or single records based on the specified condition.
Auto Commit	DDL statements are auto-commit, making permanent changes in the database	No auto-committed statements, hence no permanent changes in the database.
Classification	No further classification.	Classified into procedural DML and non-procedural DML.
Syntax	Structured and declarative	Procedural and query-based
Execution Timing	Before DML statements	After DDL statements
Examples	CREATE TABLE, ALTER TABLE, DROP INDEX	INSERT INTO, UPDATE, DELETE FROM, SELECT
Purpose	Defines and modifies the structure of a database	Manipulates and retrieves data within the established structure
Scope	Database-wide	Table or row-level
WHERE clause	DDL commands do not use a WHERE clause because filtration of records is impossible.	The DML statements can use a WHERE clause when manipulating data in a database.

DDL vs. DML: What’s the Difference?

The primary database management method hinges on utilizing database management systems. We will focus our discussion on the DDL and DML languages, highlighting their fundamental characteristics. We will perform a comparative analysis of DDL vs. DML to clarify their functionalities. Each language has unique elements that set them apart, and we will draw attention to these distinct differences. This post unravels these distinctions by providing a clearer understanding of these two main database languages. So these are the elements to focus on: 

• Purpose 
• Operations 
• Usage
• Impact
• Transactions
• Execution Timing
• Syntax
• WHERE clause 

Let’s get the ball rolling by unveiling these essential aspects:  

Purpose 

One notable element differentiating DML and DDL is their purpose within database management systems. For instance, DDL defines and modifies database structures. It creates and changes database objects like schemas, indexes, views, etc. You can apply database statements to establish the organization and database structure. When you use the DDL, you can define relationships, data types, and the rules that work for the database structure. 

In contrast, DML manipulates and retrieves data within the database structure. You can employ this database language to update existing records, query the data using select statements, insert new records into the tables, or delete records. The role of these DML statements is to enable the user to retrieve information, make changes, and manage database content. We typically require both of the two components for sufficient data operations and database management. 

Operations 

Operations is another element that reveals apparent differences as we compare DDL and DML. DDL operations define and modify the database structure. For instance, the ALTER statement enables modifications to the current objects, like altering data types, adding or removing columns, or changing constraints. We can use the DROP statement to remove or delete database objects from the schema. 

The DML operations retrieve or manipulate data within the database structure. For instance, we use the INSERT statement to add new rows or records into tables while indicating the value of every column. The UPDATE statement comes in handy for changing the existing data within a table as you update particular columns in different rows. The DELETE statement eliminates specific records and rows from your table depending on the specified conditions. Finally, the SELECT statements retrieve data for the table based on particular criteria. Such a command assists in retrieving and querying precise columns or data subsets.

Usage

You can quickly notice the differences when we analyze DDL and DML’s usage aspects. DDL comes into play when modifying or during the initial set-up of the database structure. We use it when defining or modifying the database schema, indexes, constraints, or objects. Further, we execute DDL statements less frequently than DML statements. You can use DDL when creating new tables, removing or adding columns, or defining relationships.

On the contrary, we use DML when interacting with stored data in the database daily. The action involves retrieving or manipulating data within the established structure. Again, we use DML statements when updating the existing records, inserting new data, and querying or deleting specific records based on particular criteria.

Impact

Let’s compare DDL and DML in terms of impact to unravel the difference between these two programming languages. The DDL operations impact the entire database’s structure and organization. For example, executing DDL statements like DROP, ALTER, or CREATE directly affects the database objects and schema. If you create a new table and modify the existing one, it changes the database structure automatically. Notably, these new changes that occur through DDL operations can influence the behavior and functionality of the entire database system. 

DML operations, on the other hand, manipulate the database content without modifying the structure. For instance, operations like SELECT, INSERT, UPDATE, or DELETE affect the database’s stored data instead of its structure. Typically, these DML statements affect individual records or rows within tables. As such, it enables users to retrieve or modify particular data. Notably, these operations do not change the organization or structure of the database. 

Transactions

The treatment of transactions in DDL and DML differs significantly in database management. For instance, we auto-commit DDL statements. What does this mean? We apply them immediately, and you cannot roll them back individually. If you execute DDL operations like modifying the database schema or creating a table, the changes become permanent immediately. Such actions are due to DDL operations, which alter the entire database’s behavior. 

DML statements are part of the transactions and are under transactional control. For example, DML operations, like deleting or updating records, can operate as a group within the transaction with other database operations, enabling consistency, atomicity, and isolation. One can roll back the transaction to undo the effects of the DML statements while maintaining data integrity. 

Execution Timing

We execute DDL statements before the DML operations because DDL operations modify and define the database structure. For example, when altering schemas or creating tables, you must establish these changes before data manipulation happens. Therefore, executing the DDL statements first aims to ensure the proper set-up of the database structure. 

On the other hand, we execute DML statements after processing the DDL statements. After establishing the database structure, the users can retrieve or manipulate the data within the defined structure. For example, DML statements involve deleting, updating, or inserting within the tables, which rely on the pre-existing structure defined by the DDL statements. For that reason, they can only execute after processing the DDL statements. 

Syntax

It is hard to ignore syntax when comparing DDL and DML. The role of syntax is to define how we structure and use commands within a database management system. First, the DDL statements follow a syntax that captures keywords like DROP, ALTER or CREATE. For instance, if you wish to create a table using DDL, the syntax prompts you to specify the table name, column names, and their respective data types.

Concerning DML statements, they manipulate and retrieve data within the established data structure. They rely on keywords such as SELECT, DELETE, UPDATE, and INSERT. In this case, the syntax specifies the target table, column for modification or retrieval, and any other criteria. 

WHERE Clause 

The WHERE clause is a critical component in database management systems. However, the purpose and usage vary. In fact, the WHERE clause does not apply in the DDL statements. DDL statements like CREATE only define and modify tables and database structures. In this case, the WHERE clause does not apply because it is acting on the structural aspect instead of manipulating particular data. 

Notably, the WHERE clause is a requirement in the DML statements. DML statements like UPDATE manipulate the database’s data. In this case, we employ the WHERE clause to specify the conditions that filter data for querying, deletion, etc.  

DDL vs. DML: Must-Know Facts 

• DDL defines and modifies the database’s structure by creating, altering, and dropping database objects, while DML is used to manipulate and retrieve data within this established structure.
• DDL includes statements like ALTER focus on creating, modifying, and deleting database objects, while DML statements like DELETE perform operations on data within tables.
• DDL focuses on the database structure, whereas DML focuses on manipulating and retrieving data.
• We execute DDL statements before DML statements to establish the database structure before data manipulation.
• DDL changes impact the entire organization and structure of the database, while DML changes affect individual data records within tables.

DDL vs. DML: Which Should You Choose?

Data Manipulation Language (DML) plays a significant role in database management systems. DDL serves as the scaffolding for database structure, enabling users to define and modify the entire architecture of a database, from schemas and tables to constraints and indexes. In contrast, DML offers an efficient way to interact with the data within the structured database, whether that means inserting, updating, deleting, or retrieving data. They both have unique functionalities and, when combined, they offer a comprehensive database management system.

The choice between DDL and DML will significantly depend on your specific needs and preferences. If you’re looking to build or alter the very structure of your database, DDL’s features will be your go-to. It will provide the tools to create, modify, and define your database objects and structure, setting the stage for data manipulation. On the other hand, if your task involves working directly with the data stored within the database, DML has got you covered. From updating and deleting to inserting and retrieving data, DML allows you to manage your database content efficiently.

However, it’s essential to remember that neither DDL nor DML needs to be exclusively chosen. They are most effective when used in concert. You might need DDL to set up your database and create the necessary tables, constraints, and indexes. Once that’s done, DML comes into play, helping you to manage the data within this structure. Their roles are complementary and synergistic. 

While DDL creates the platform, DML performs on it. So, it’s more than choosing one over the other and understanding how to leverage each based on the task. Both are crucial components of effective database management, and the ultimate choice lies in your hands based on your needs and the task at hand.