Don't Let Messy Data Ruin Your Analysis: SQL Cleaning Tips
Most SQL tutorials teach you syntax. This one teaches you how to actually clean a messy, real-world e-commerce dataset the way it happens in production — problem by problem, not by guessing.In this video, you'll learn how to find and fix the most common data quality issues analysts face on the job using nothing but SQL:
✅ Duplicate rows — using GROUP BY, ROW_NUMBER() OVER (PARTITION BY...) and safe deletion with ROWID
✅ NULL values — how to count them and decide when to fix vs. leave them alone
✅ Leading/trailing spaces and multiple spaces between words — TRIM() and REGEXP_REPLACE()
✅ Hidden whitespace characters (tabs, line breaks, non-breaking spaces) — CHR() and nested REPLACE()
✅ Unwanted special characters in text fields — REGEXP_LIKE() and REGEXP_REPLACE()
✅ Inconsistent text casing (mumbai / Mumbai / MUMBAI) — INITCAP() and UPPER()
✅ Placeholder/junk values like "N/A" or "Test"
✅ Invalid email addresses — validating structure with REGEXP_LIKE()
This is real SQL for data analysis — the kind of query pattern interviewers ask about and the kind of judgment call you'll actually make at work. The goal isn't to change as much data as possible. It's to make the data trustworthy.
Fix what you know. Don't guess what you don't. That's the whole philosophy behind this video.
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🔗 RESOURCES
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💼 Paid Training Sessions → https://www.dataprofy.com/sqltraining/
📘 My Book (mentioned in this video) → https://dataprofy.com/product-category/ebook/
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