Data platforms are getting smarter โ 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:
๐น Why optimization becomes a visibility problem at enterprise scale ๐น How Predictive Optimization actually works under the hood ๐น Z-Ordering vs Liquid Clustering โ and how Predictive Optimization handles both differently ๐น 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 โ especially those who have already enabled Predictive Optimization at scale.
๐ [https://medium.com/p/37a11392d2af?postPublishedType=initial]
