⚙️ Unity Catalog: Creating the Silver Layer in Databricks with Python

What happens when your raw data doesn’t meet expectations? You filter. You clean. You level up.

And sometimes, you drop 20,000 records to get there.

In this week’s video, I walk through building the Silver layer in our Unity Catalog pipeline. Following last week's Bronze layer setup, we now address data quality—specifically the expectation for valid, non-null values in critical fields like reasonDescription.

📉 You'll see how we go from 29k raw rows down to 8.3k validated records—and why that’s not a bad thing.

In this walkthrough, I’ll cover:

🧪 How to define expectations in Python

✅ Why data quality logic belongs in the Silver layer

🔍 How to build trust in your downstream analytics and ML

💡 And why clean > complete when scaling production pipelines

🎥 Watch the full video here:

Next up: Gold layer, business logic, and production-ready data models.

💬 Have questions or facing similar issues in your own pipeline? Drop a comment or DM me.

📩 And if you're leading a team working through this stuff in Databricks—we should connect.

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