Explanation:
The continuous processing pattern is the best choice for this scenario because it enables real-time data processing with minimal latency, which is critical for immediate fraud detection. While the event-time processing pattern and stateful processing pattern are important for handling late-arriving data and maintaining transaction state, respectively, the continuous processing pattern directly addresses the need for real-time monitoring. The micro-batch processing pattern, although useful for certain scenarios, does not meet the requirement for minimal latency as effectively as the continuous processing pattern.
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In the context of Azure Databricks, Structured Streaming and Delta Lake enable several design patterns for efficient data processing. Consider a scenario where a financial services company needs to monitor transactions in real-time to detect fraudulent activities immediately. The solution must process data continuously, support event-time processing to handle late-arriving data, and maintain state to track transactions over time. Given these requirements, which design pattern would be the BEST choice to implement? Choose the correct option from the following:
A
Micro-batch processing pattern, as it processes data in small batches, providing a balance between latency and throughput.
B
Continuous processing pattern, as it allows for real-time data processing with minimal latency, suitable for immediate fraud detection.
C
Event-time processing pattern, as it ensures accurate processing of data based on when events actually occurred, crucial for handling late-arriving transaction data.
D
Stateful processing pattern, as it maintains the state of transactions over time, essential for tracking and analyzing fraudulent activities across multiple transactions.
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