Data platforms are getting smarter — but are we asking the right questions about what that means for data engineering?

I wrote about how Databricks Predictive Optimization is shifting the role of data engineers from reactive maintenance to autonomous operations. The article covers:

🔹 Why optimization becomes a visibility problem at enterprise scale 🔹 How Predictive Optimization actually works under the hood 🔹 Z-Ordering vs Liquid Clustering — and how Predictive Optimization handles both differently 🔹 Where automation wins, and where engineering judgment still matters

The question I keep coming back to: how much of data engineering optimization will remain manual five years from now?

Would love to hear how others in the community are approaching this — especially those who have already enabled Predictive Optimization at scale.

👉 [https://medium.com/p/37a11392d2af?postPublishedType=initial]