How to handle schema evolution in a CDC analytics pipeline?
This tests your ability to design resilient CDC pipelines. A strong answer outlines automated schema detection, using a flexible format like Avro, and enforcing governance with a schema registry. A red flag is proposing manual fixes for every change.
This tests your ability to design resilient data pipelines that automatically handle schema drift from a CDC source. A strong answer details a three-step process: using a schema registry to detect and version changes, storing data in an evolution-friendly format like Avro or Parquet, and ensuring downstream jobs are configured for compatibility. The main red flag is proposing manual fixes or pipeline restarts for every schema change, which indicates a brittle, non-scalable design.
Read the original → branchboston.com
- #data engineering
- #cdc
- #schema evolution
- #system design
- #analytics
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