Data Manipulation Language (DML)

Discipline: Data Analyst | Module: Module 2: Enterprise Structured Query Language (SQL) & Relational Databases | Estimated Study Time: 8 Hours

Welcome to Data Manipulation Language (DML). This topic delivers foundational and advanced concepts designed for production engineering and real-world workflows.

Key Learning Objectives

1. Mutation Operations (INSERT INTO, UPDATE…SET, DELETE FROM)
2. Merging Datasets (UPSERT Patterns, MERGE INTO statements)

Detailed Curriculum Breakdown

Mutation Operations (INSERT INTO, UPDATE…SET, DELETE FROM)

Explore the fundamental principles, real-world patterns, and best practices for Mutation Operations (INSERT INTO, UPDATE…SET, DELETE FROM). Practice hands-on implementations to master these concepts.

// Code Example: Mutation Operations (INSERT INTO, UPDATE...SET, DELETE FROM)
// Implement verified patterns for production use
console.log("Mastering Mutation Operations (INSERT INTO, UPDATE...SET, DELETE FROM)");

Merging Datasets (UPSERT Patterns, MERGE INTO statements)

Explore the fundamental principles, real-world patterns, and best practices for Merging Datasets (UPSERT Patterns, MERGE INTO statements). Practice hands-on implementations to master these concepts.

// Code Example: Merging Datasets (UPSERT Patterns, MERGE INTO statements)
// Implement verified patterns for production use
console.log("Mastering Merging Datasets (UPSERT Patterns, MERGE INTO statements)");

Practical Application & Exercises

1. Architecture Review: Evaluate how Data Manipulation Language (DML) integrates with upstream and downstream systems.
2. Implementation Challenge: Build a functional prototype demonstrating each of the subtopics.
3. Validation & Testing: Verify performance and error handling under edge-case scenarios.

Summary Checklist

• Studied foundational architecture for Data Manipulation Language (DML)
• Completed practical coding challenge
• Validated edge cases and error handling routines