DML (Data Manipulation Language) commands are used to perform actions on data within a database. These include inserting, updating, deleting, and selecting data. Understanding DML is crucial for any SQL developer.
DML statements are the core of interacting with data in a relational database management system (RDBMS). They allow you to modify existing data, add new records, and retrieve information. The most common DML commands are INSERT, UPDATE, DELETE, and SELECT. INSERT is used to add new rows to a table, UPDATE modifies existing rows, and DELETE removes rows. SELECT, while technically a DML command, is often considered separately as it retrieves data without changing the database structure. These commands are essential for tasks like managing customer information, tracking inventory, or analyzing sales data.
DML is fundamental to any database application. It allows developers to interact with the data, update it, and retrieve information for various reporting and analysis tasks. Without DML, databases would be static and unusable.
The primary SQL Data Manipulation Language (DML) commands are INSERT, UPDATE, DELETE, and SELECT. INSERT adds new rows to a table, UPDATE modifies existing rows, DELETE removes rows, and SELECT retrieves data for analysis without changing the table’s structure.
Although SELECT is technically part of DML, it’s frequently discussed on its own because it only reads data. INSERT, UPDATE, and DELETE all mutate the data set, whereas SELECT focuses on querying and reporting. This functional difference makes developers and documentation separate SELECT-related optimization and best practices from write-focused operations.
Galaxy’s modern SQL editor speeds up writing INSERT, UPDATE, DELETE, and SELECT statements through AI-powered autocompletion, context-aware query optimization, and real-time schema insights. The AI copilot can suggest correct syntax, highlight potential mistakes, and even refactor queries when the data model changes—reducing errors and letting teams share vetted DML scripts in one place instead of pasting SQL into Slack or Notion.