🔍 Data Cleaning Techniques Using SQL

Data cleaning is a critical step in the data preparation process. Whether it is analytics, business intelligence, or data engineering, clean data ensures more precise and reliable insights.

1️⃣ Convert Text to Lower/Upper Case
Ensure consistency in categorical fields like names or categories.

-- Convert to lowercase
SELECT LOWER(column_name) AS cleaned_column FROM table_name;

-- Convert to uppercase
SELECT UPPER(column_name) AS cleaned_column FROM table_name;

2️⃣ Remove Extra Spaces from Text Fields Trim leading/trailing spaces using TRIM()

SELECT TRIM(column_name) AS cleaned_column FROM table_name;

3️⃣ Convert Date Strings to a Consistent Format
Transform text-based dates into a usable date format:

SELECT STR_TO_DATE(column_name, '%m/%d/%Y') AS formatted_date FROM table_name;

4️⃣Identify & Manage Outliers

Filter numeric values within a defined range:

SELECT * FROM table_name 
WHERE column_name BETWEEN lower_limit AND upper_limit;

5️⃣ Remove Special Characters

Strip out unwanted symbols using regular expressions:

SELECT REGEXP_REPLACE(column_name, '[^a-zA-Z0-9 ]', '') AS cleaned_column 
FROM table_name;

6️⃣ Standardize Categorical Values

Unify inconsistent text representations:

UPDATE table_name
SET column_name = 'Male'
WHERE column_name IN ('M', 'male');

7️⃣ Replace NULLs with Default Values

SELECT COALESCE(column_name, 'DefaultValue') AS column_name 
FROM table_name;

8️⃣ Delete Duplicate Rows

WITH CTE AS (
    SELECT *,
           ROW_NUMBER() OVER (PARTITION BY column1, column2 ORDER BY id) AS row_num
    FROM table_name
)
DELETE FROM table_name
WHERE id IN (
    SELECT id FROM CTE WHERE row_num > 1
);

💡 Clean data = better insights.
SQL makes it easy to standardize, validate, and transform your data—right where it lives.

If you found this helpful, feel free to share or drop a comment with your favorite data cleaning tip! 💬