Introduction

Data transformation is a key step in SQL for data analysis where raw data is converted into a structured and meaningful format. After cleaning the data, transformation helps in reshaping, combining, and preparing data for reporting and insights. In this lesson, you will learn how to transform data using SQL.

What is Data Transformation in SQL

Data transformation involves modifying data to make it suitable for analysis. This includes changing formats, creating new columns, combining data, and applying calculations.

Creating New Columns

You can create new columns using calculations or expressions.

Example:
SELECT name, salary, salary * 12 AS annual_salary
FROM employees;

This creates a new column for annual salary.

Using CONCAT for Combining Data

You can combine multiple columns into one using CONCAT.

Example:
SELECT CONCAT(first_name, ‘ ‘, last_name) AS full_name
FROM employees;

This creates a full name column.

Changing Data Format

You can format data using SQL functions.

Example:
SELECT DATE_FORMAT(order_date, ‘%Y-%m’) AS order_month
FROM orders;

This converts date into year-month format.

Using CASE for Transformation

CASE can be used to transform data into categories.

Example:
SELECT name,
CASE
WHEN salary > 50000 THEN ‘High’
ELSE ‘Low’
END AS salary_category
FROM employees;

Converting Data Types

You can convert data types using CAST.

Example:
SELECT CAST(salary AS SIGNED) FROM employees;

Why Data Transformation is Important

Data transformation helps you:

• Prepare data for reporting
• Create meaningful metrics
• Improve data readability
• Enable better decision-making

Real-World Use Cases

• Creating KPIs from raw data
• Combining customer names
• Formatting dates for reports
• Categorizing business data

Best Practices

• Keep transformations simple and clear
• Use meaningful column names
• Avoid unnecessary calculations
• Test transformations on sample data

Common Mistakes

• Overcomplicating transformations
• Using incorrect data formats
• Ignoring data types
• Not validating transformed data

Summary

In this lesson, you learned how to transform data using SQL by creating new columns, combining data, and formatting values. Data transformation is essential for turning raw data into actionable insights. In the next lesson, you will learn how to work with dates in SQL.

FAQs

1. What is data transformation in SQL?
It is the process of converting raw data into a structured format for analysis.

2. Which SQL functions are used for transformation?
Functions like CONCAT, CASE, CAST, and DATE_FORMAT are commonly used.

3. Why is data transformation important?
It helps in creating meaningful insights and reports.

4. Can we create new columns in SQL?
Yes, using expressions and calculations.

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