🔍 You Can’t Control the Mess — But You Can Control How You Clean It: Handling Data Quality in Real-World Pipelines

One of the biggest challenges data engineers face isn’t about scaling systems or optimizing performance — it’s dealing with data we don’t control.

Photo by Eastman Childs on Unsplash

We’ve all been there.

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You’re tasked with building a robust data pipeline, but the data source is a third-party system, a legacy database, or an API that sometimes decides to go rogue. You start noticing:

• Null values in required fields
• Date columns in the wrong format
• Duplicate entries
• Values outside the expected range
• And the classic: a last-minute schema change with no warning

And yet… you’re still expected to produce accurate dashboards and reliable analytics for the business.

Welcome to real-world data engineering.

🎯 The Harsh Reality: You Don’t Own the Source

Most data pipelines rely on input from systems we don’t own or influence. These could be:

• External vendors
• Operational systems managed by another team