Data Manipulation Language (DML)

Data plays an important role in every industry, including education, business, healthcare, and technology. However, to make this data useful and meaningful, it must be properly managed and controlled within databases. This is where Data Manipulation Language (DML) becomes essential. DML provides a set of commands that allow users to insert, update, retrieve, and delete data stored in database tables. It ensures that the data remains accurate, up to date, and easy to access when needed. In this blog, you will explore what DML is, its key features, how it works, and the main types of DML commands used in database management.

Table of Contents:

• What is Data Manipulation Language (DML)?
• Key Characteristics of Data Manipulation Language (DML)
• Types of DML Commands in SQL
• Advantages of Data Manipulation Language (DML)
• Procedural vs Non-Procedural DML
• Difference Between DML and Other SQL Commands
• Challenges in Data Manipulation Language
• Best Practices for Using DML Commands in SQL
• Real-World Use Cases of Data Manipulation Language (DML)
• Conclusion

What is Data Manipulation Language (DML)?

Data Manipulation Language (DML) is a group of commands used to manage and work with data in storage systems like databases, spreadsheets, or data files. It allows users to directly interact with the data by adding new entries, updating existing records, retrieving specific information, or deleting unnecessary data. Widely used by data professionals, developers, and system administrators, DML plays a key role in ensuring data remains accurate, current, and ready for use in various applications. It is very important in areas like reporting, data analysis, and running business processes efficiently.

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Key Characteristics of Data Manipulation Language (DML)

Here are some key characteristics of DML that make it an essential component of data management:

1. Data Manipulation

DML is specifically designed for managing and interacting with data directly within a database. With DML, you can:

• Add new data (like adding a new record).
• Change existing data (like changing a value).
• Read or retrieve data (like reading a report).
• Delete data (like deleting old records).

2. Ease of Use for Data Manipulation

DML commands are typically easy to read, even for beginners. You do not have to be good at coding to:

• View or filter data using simple queries.
• Add new entries into a table.
• Perform small modifications to existing records with ease.
• This ease of use allows people from different backgrounds to work with data.

3. Non-Structural

DML simply works with the contents of a data system, and it does not affect the design or structure. For example:

• You cannot create or remove a table using DML.
• You can only manipulate the data within that table.
• The structure is defined and handled separately via design or commands.

4. Temporary Until Saved

Most changes executed with DML commands are temporary until you have committed to saving them intentionally. 

• You can commit your changes if you are satisfied with them.
• If you don’t want to save your changes, you can simply roll them back, even if the change was accidental.
• This allows peace of mind and flexibility when working with important data.

5. Supports Filters and Conditions

DML makes it easy to apply filters and conditions when working with data. This allows users to target specific records based on criteria. For example:

• Display only students who passed.
• Update only products that are out of stock.
• Delete records that are older than 2 years.
• This capability helps provide a much more focused approach for data handling.

Types of DML Commands in SQL

SQL contains four principal types of Data Manipulation Language (DML) commands, which control the data in a database table. Each DML command accomplishes a different task in the management of data. 

Let us first create a sample table that we will use to demonstrate the syntax and working of various DML commands.

-- Creating a table called INTELLIPAAT
CREATE TABLE INTELLIPAAT (
    id INT PRIMARY KEY,
    name VARCHAR(50),
    marks INT
);
-- Inserting the data into it
INSERT INTO INTELLIPAAT (id, name, marks) VALUES (1, 'Riya', 92);
INSERT INTO INTELLIPAAT (id, name, marks) VALUES (2, 'Aman', 88);
INSERT INTO INTELLIPAAT (id, name, marks) VALUES (3, 'Neha', 95);

1. SELECT Command

The SELECT command is used to look at or get data from a table. The SELECT command will not modify data in the table, but it will only show data to the user. The SELECT command is particularly useful for showing all records in the table or limiting the output to some records based on conditions. 

Syntax:

SELECT column1, column2 FROM table_name;

Example:

SELECT * FROM INTELLIPAAT;

Output:

Explanation:  Here, this query is used to show or fetch the result to the user.

2. INSERT Command

The INSERT command is used to add new records to the table. It helps populate the table with fresh data and is key to building a dataset.

Syntax:

INSERT INTO table_name (column1, column2, ...) VALUES (value1, value2, ...);

Example:

INSERT INTO INTELLIPAAT (id, name, marks) VALUES (4, 'Amit', 85);

Output:

Explanation: Here, this query is used to add new data into the table.

3. UPDATE Command

The UPDATE command is used to modify or change the data that is present in the existing table. It helps keep data accurate by modifying specific values based on given conditions.

Syntax:

UPDATE table_name SET column1 = value1 WHERE condition;

Example:

UPDATE INTELLIPAAT SET marks = 90 WHERE name = 'Amit';

Output:

Explanation: Here, this query modifies the marks of student ‘Amit’ from 85 to 90.

4. DELETE Command

The DELETE command is used to remove one or more records from a table. This command is useful when you want to get rid of old, duplicate, or incorrect data from a specific table. 

Syntax:

DELETE FROM table_name WHERE condition;

Example:

DELETE FROM INTELLIPAAT WHERE name = 'Amit';

Output:

Explanation: Here, this query is used to remove the record ‘Amit’ from the table.

Note: Without the WHERE clause, all the records may be deleted

Advantages of Data Manipulation Language (DML) 

Data Manipulation Language has different benefits of working with data stored in a database.

• Simple Data Manipulation: You can add, view, modify, or delete data instantly, without risking the initial data structure.
• Real-Time Data Updates: DML commands allow immediate changes, with current data anytime. 
• Saves Time: With simple commands like SELECT, UPDATE, data manipulation becomes faster and cuts down on time.
• Supports Automation: DML is frequently employed in scripts or applications for automation purposes of data handling.

Procedural vs Non-Procedural DML

Difference Between DML and Other SQL Commands

Challenges in Data Manipulation Language

Let’s explore the common challenges in Data Manipulation Language that can impact data integrity, performance, and error handling.

1. Data Loss: A simple miss or mistake could cause an important piece of information to be lost. 
2. Performance Issues: Long and complex queries can slow a large database. 
3. Human Error: A simple wrong character could miss a change to the data. 
4. Security Issues: While deleting and updating data, one could accidentally expose data, events, and other sensitive information with improper use. 
5. Need to have conditions: If a user forgets to include a WHERE condition, many or even all records in the database could be unintentionally altered.

Best Practices for Using DML Commands in SQL

Let’s explore some best practices for using DML commands in SQL to ensure your data stays accurate, safe, and well-managed.

1. Always Use the WHERE Clause: Avoid affecting all records unintentionally by including a proper WHERE condition in your UPDATE or DELETE command.
2. Backup Before Modifying Data: Create a backup of your data before running any critical UPDATE or DELETE commands to prevent permanent loss in case of an error.
3. Test on Sample Data First: Practice your DML commands on test or dummy tables before applying them to production data to avoid costly mistakes.
4. Keep Transactions Short and Controlled: Use COMMIT and ROLLBACK effectively to manage your changes. Keeping transactions short reduces locking issues and improves performance.
5. Use Descriptive Column Names: Choose clear and meaningful column names to make your queries easier to write, understand, and maintain over time.

Real-World Use Cases of Data Manipulation Language (DML)

1. E-Learning Portals: DML is used to add and update course details, manage student records, and track learning progress.
2. Banking Systems: DML helps add and update customer accounts, modify balances, and remove records of closed accounts.
3. E-commerce Sites: DML supports adding new products, updating listings and prices, and deleting items from expired or discontinued categories.
4. Healthcare Databases: DML is applied to update patient information, add medical reports or test results, and delete outdated or irrelevant records.
5. Attendance Systems: DML enables recording daily attendance, updating logged hours, and deleting incorrect or duplicate entries.

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Conclusion

Data Manipulation Language (DML) is a key component of SQL that allows users to manage and modify the data stored in a database. With DML, you can create new records, extract specific information, update existing entries, and delete data that is no longer needed using straightforward and easy-to-learn commands. DML is essential for managing everyday data tasks and plays a vital role in real-world database operations.

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Data Manipulation Language (DML) – FAQs