AIInterviewTraining logoAIInterview/Training
← SQL & Data Engineering / 20
mediumDatabricksSnowflakeGoogle

How do you handle schema evolution in a data pipeline or lakehouse without breaking consumers?

A new column is safe; a rename quietly corrupts every dashboard downstream. The signal is knowing which changes stay compatible, enforcing data contracts in CI, and running expand-contract migrations for the breaking ones.

Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.

A new column is safe; a rename quietly corrupts every dashboard downstream. The signal is knowing which changes stay compatible, enforcing data contracts in CI, and running expand-contract migrations for the breaking ones.

more free answers with an account · no card
UP NEXT ON YOUR JOURNEY
Next in this trackHow do you join two streams (or a stream to a table) in a streaming system?Next in this trackExplain SQL set operations (UNION/INTERSECT/EXCEPT) and NULL handling pitfalls.Next in this trackWhat is data skew in a distributed job (Spark), and how do you fix it?
DISCUSSION · 0

No comments yet — be the first to share your approach.