SQL Data Analytics • Lesson 10

Data Cleaning with SQL

Learn how analysts clean messy text, standardize values, handle missing data, and prepare database fields for reliable analysis.

Your progress0%

0 of 8 concepts completed

Complete the activity and related MCQ to unlock the next concept.

Concept 1WHY CLEAN

Why Data Cleaning Matters

Real-world data is rarely perfect. Names contain extra spaces, cities use inconsistent capitalization, emails are mixed case. Before any analysis, these inconsistencies must be standardized or every downstream number becomes unreliable.

Interactive Data

Dirty & Clean

The same four rows, before and after standardizing the name, city and email columns.

ROWS

COLUMNS CLEANED

NULL VALUE

Before cleaning

name	city	email
" Rahul "	"delhi"	"RAHUL@EMAIL.COM"
"PRIYA"	" Mumbai "	"priya@email.com"
" Amit"	"PUNE"	"AMIT@EMAIL.COM"
"Neha "	"delhi"	"neha@email.com"

After cleaning

clean_name	clean_city	clean_email
Rahul	DELHI	rahul@email.com
PRIYA	MUMBAI	priya@email.com
Amit	PUNE	amit@email.com
Neha	DELHI	neha@email.com

Learn by Seeing

Dirty data patterns

'  Rahul  '
'delhi' vs 'DELHI'
' Mumbai '
'RAHUL@EMAIL.COM'

Extra spaces, mixed case and inconsistent formatting are the most common data-quality issues.

Pick a value — see how it would group:

GROUP BY city

"delhi" → its own row

Without cleaning, each spelling/case becomes a separate group, which breaks any per-city totals.

Interactive Activity

Why must data be cleaned before analysis?

Hint: Think about what happens if 'delhi' and 'DELHI' are treated as different values.

SQL Analyst Mindset

Clean first, analyze second

Data cleaning is not glamorous, but it is what separates reliable analysis from misleading numbers. A quick TRIM, UPPER or COALESCE in the SELECT clause prevents entire categories from silently splitting in your reports.

Inspect

Look for spaces, mixed case, NULLs.

Standardize

TRIM, UPPER, LOWER, REPLACE.

Handle NULLs

IS NULL, IS NOT NULL, COALESCE.

Verify

Do cleaned values group as expected?

Frequently Asked Questions

SQL Data Cleaning — FAQs

What is data cleaning in SQL?

It is the process of standardizing text, fixing values, and handling missing data so that grouping, joining, and comparisons behave correctly.

Does TRIM change the middle of a string?

No. TRIM only removes leading and trailing spaces; inner spaces remain untouched.

Why use UPPER and LOWER?

To standardize capitalization so that 'delhi' and 'DELHI' are treated as the same value.

When should I use COALESCE?

When NULLs would break a report — COALESCE replaces them with a fallback value you choose.

Lesson Summary

What you learned

TRIM()

Remove leading and trailing spaces.

UPPER() / LOWER()

Standardize capitalization.

REPLACE()

Substitute text values.

IS NULL / COALESCE()

Detect and replace missing values.