Hi everyone,

I’m dealing with a scenario combining Delta Live Tables, CDC ingestion, and streaming pipelines, and I’ve hit a challenge that I haven’t seen clearly addressed in the docs.

Some Context:

• Source is an upstream system emitting CDC events (insert/update/delete)
• Data is ingested via Auto Loader into a bronze layer
• From there, I’m using DLT to build silver tables with merge logic (SCD Type 1)
• The pipeline runs in continuous/streaming mode

The issue is around schema evolution, especially breaking changes:

• column type changes (e.g., int → string)
• column drops or renames
• nested structure changes

While Auto Loader can handle schema evolution to some extent, downstream DLT transformations (especially merges) tend to fail or behave unpredictably when these changes occur.

My concerns:

• avoiding pipeline failures in production
• maintaining data quality and historical consistency
• not overcomplicating the pipeline with excessive manual handling

Questions:

• What’s the best pattern to handle breaking schema changes in this setup?
• Do you isolate schema evolution strictly in bronze and enforce contracts from silver onward?
• Has anyone implemented schema versioning or schema registry-like patterns with DLT?
• How do you balance flexibility (auto evolution) vs governance (strict schemas)?

Would really appreciate insights from anyone who has dealt with this in production.

Thanks!