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How can you effectively manage schema evolution in a streaming data ingestion pipeline using Delta Lake on Databricks to prevent data corruption and ensure downstream systems adapt gracefully to schema changes?
A
Implementing custom Spark Structured Streaming logic to detect schema changes in incoming data streams and update Delta tables accordingly
B
Configuring Delta Lake to ignore schema changes, relying on downstream systems to handle variations in data structure
C
Enabling Delta Lake‘s schema enforcement feature to automatically reject records that don‘t match the table schema and manually updating the schema for significant changes
D
Utilizing Delta Lake‘s schema evolution capabilities to merge schema changes automatically, using mergeSchema options on write operations
