The Databricks Schema Management Guide: When to Evolve and When to Enforce

The strategic pattern that turns schema evolution from a 40-hour debugging disaster into controlled change management

Too many Databricks data engineering teams enable schema evolution everywhere without thinking twice.

Whenever a pipeline breaks because of a schema change, the default response is: “Let’s enable schema evolution so this never happens again.”

But here’s the problem: automatic schema evolution doesn’t prevent failures. It just delays them and makes them wo…