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?

Real Exam

Implementing custom Spark Structured Streaming logic to detect schema changes in incoming data streams and update Delta tables accordingly

6.7%

Configuring Delta Lake to ignore schema changes, relying on downstream systems to handle variations in data structure

3.3%

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

11.7%

Utilizing Delta Lake‘s schema evolution capabilities to merge schema changes automatically, using mergeSchema options on write operations

78.3%

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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?