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The Art and Science of Data Cleaning: A Data Engineer’s Battle-Tested Guide to SQL Excellence
The Reality Check: A Personal Introduction
It was 3 AM, and I was staring at my screen, debugging a production pipeline that had just failed. The culprit? Dirty data. A simple misformatted date field had cascaded into a series of analytics errors, causing our executive dashboard to show incorrect revenue projections. That night changed my perspective on data cleaning forever.
Hey there! I’m Mayur, a Senior Data Engineer with 8+ years of experience in the trenches of data warehousing and analytics. Today, I’m sharing the battle-tested strategies that have saved countless projects and, quite frankly, my sanity.
Why Another Article on Data Cleaning?
Let’s be honest — most articles about data cleaning are dry, theoretical, and disconnected from reality. They tell you WHAT to do but not WHY or HOW it impacts your business. This isn’t just another SQL tutorial; it’s a practical guide born from real-world chaos and solutions.
The Hidden Cost of Dirty Data: A Business Reality
Before we dive into code, let me share a stark reality:
- IBM estimates that poor data quality costs the US economy…
