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Answer: Update the Kafka consumer configuration in the job to handle the new schema, modify the transformation logic to match the new schema, and rerun the job.
Adapting to a new Kafka schema involves updating the consumer configuration and modifying the data processing logic to handle the new fields and data types. This ensures that the job can continue processing the real-time data without interruption.
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Consider a Databricks job that processes real-time data from a Kafka stream, performs transformations, and loads the results into a database. The job has recently started failing due to changes in the Kafka schema. Describe the steps you would take to adapt the job to handle the new schema and ensure continuous operation.
A
Update the Kafka consumer configuration in the job to handle the new schema, modify the transformation logic to match the new schema, and rerun the job.
B
Switch to a different data source that matches the old schema, keep the job configuration unchanged, and rerun the job.
C
Increase the Kafka consumer timeout, assume the schema will resolve itself, and rerun the job.
D
Disable the Kafka stream processing step, manually process the data offline, and rerun the job.