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Answer: Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
Introducing a new MapReduce job to apply sensor calibration to raw data, and ensuring all other MapReduce jobs are chained after this, is a cleaner approach. It allows the calibration to be handled in a single step at the beginning of the pipeline, making the process more maintainable and efficient. Modifying every transformMapReduce job (Option A) to apply sensor calibration would be cumbersome and could introduce complexity and bugs. The new MapReduce job ensures that the calibration is systematically carried out before any other processing, thus avoiding the omission of the sensor calibration step in the future.
Author: LeetQuiz Editorial Team
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In your role as a system architect for analyzing seismic data, you have designed an ETL (Extract, Transform, Load) process utilizing a series of MapReduce jobs within an Apache Hadoop cluster. Currently, your ETL process takes several days to complete due to the computationally intensive nature of certain steps. Recently, you identified that a crucial step for sensor calibration was overlooked. How should you modify your ETL process to ensure that sensor calibration is systematically included in future operations?
A
Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
B
Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
C
Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
D
Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
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