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Answer: Implement Spark Structured Streaming with dynamic scaling and rate limiting to adjust processing based on incoming data volumes.
Implementing Spark Structured Streaming with dynamic scaling and rate limiting is the optimal approach for managing fluctuating data volumes with minimal latency. This method enables the pipeline to automatically adapt processing resources according to the volume of incoming data, ensuring efficient operation without excessive resource use. Dynamic scaling facilitates cluster adjustment based on workload, accommodating data spikes without latency issues. Rate limiting regulates data processing speed, avoiding overload and maintaining performance during peak periods. Static batch processing intervals (Option C) lack the flexibility for real-time data volume changes. Sole reliance on Apache Kafka (Option A) may complicate the pipeline and introduce bottlenecks. Manual cluster adjustments (Option B) are inefficient, requiring ongoing oversight and potentially causing processing delays. Thus, Spark Structured Streaming with dynamic scaling and rate limiting stands out as the most effective solution for real-time data processing challenges.
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How would you architect a real-time data processing pipeline using Databricks and Apache Spark Structured Streaming to efficiently handle fluctuating data volumes with minimal latency?
A
Rely exclusively on Apache Kafka for real-time data buffering and batch process the data at fixed intervals.
B
Manually adjust the cluster size based on expected data volume increases.
C
Utilize static batch processing intervals to manage data loads predictably.
D
Implement Spark Structured Streaming with dynamic scaling and rate limiting to adjust processing based on incoming data volumes.
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