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Answer: Utilizing Delta Lake‘s schema evolution capabilities to merge schema changes automatically, using mergeSchema options on write operations
The most effective approach to manage schema evolution in a streaming data ingestion pipeline using Delta Lake on Databricks is to utilize Delta Lake‘s schema evolution capabilities. This method automatically merges schema changes with the existing schema during write operations, ensuring data integrity and compatibility with downstream systems. Other options, such as manual schema updates or ignoring schema changes, either introduce unnecessary complexity or risk data corruption and inconsistencies.
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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
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