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You're tasked with designing a real-time data processing solution in Databricks capable of handling streaming data with fluctuating volumes. Which approach guarantees scalability and performance during peak data influx?
A
Preprocess data using Azure Functions before ingesting it into Databricks to reduce load.
B
Archive incoming data during peak periods and process it during off-peak hours.
C
Implement a static number of Spark Streaming receivers regardless of data volume.
D
Use structured streaming with auto-scaling Databricks clusters based on the streaming query's processing rate.
