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In Delta Lake, managing datasets with frequently changing schemas while preserving historical data integrity is essential for analytics. What is the best approach to handle schema evolution for adding new columns and changing data types without affecting existing queries?
A
Creating versioned copies of tables for each schema change, redirecting queries to the appropriate version
B
Utilizing a view layer on top of raw Delta tables to abstract schema changes from end-users
C
Enabling Delta Lake‘s schema merging feature on write operations to automatically accommodate schema changes
D
Implementing schema validation logic in Spark before data ingestion to prevent incompatible changes