Databricks Certified Data Engineer - Professional

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How can you optimize a Spark Structured Streaming application for low-latency processing of financial transactions to ensure minimal processing time per micro-batch without compromising stateful accuracy?

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Explanation:

Stateful Accuracy: The mapGroupsWithState function ensures complete stateful accuracy by maintaining and updating state information across micro-batches, which is crucial for financial transactions.
Minimize Processing Time: Using mapGroupsWithState with a low trigger interval reduces processing time per micro-batch by processing data in a stateful manner within each micro-batch.
Low-Latency Streaming: For applications requiring low-latency, mapGroupsWithState with a low trigger interval processes data quickly and efficiently, reducing latency.
Spark Structured Streaming: Designed for stateful processing, mapGroupsWithState leverages Spark's optimized processing engine for streaming data, making it ideal for financial transaction processing.