Your question is Data Quality and Schema Evolution. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You're designing a high-volume data lake pipeline and want a clean approach for handling changing upstream schemas without breaking downstream consumers. You also need a practical way to catch bad data early and keep raw and curated layers usable over time.
How do you ensure data quality and schema evolution in a high-volume data lake architecture?