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PROFESSIONAL-DATA-ENGINEER · Question #141

You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process…

The correct answer is B. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this. Explanation/Reference: It is a cleaner approach with single job to handle the calibration before the data is used in the pipeline. Second, doing this step in later stages can be complex and maintenance of those jobs in the future will become challenging.

Submitted by tyler.j· Mar 30, 2026Designing data processing systems

Question

You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

Options

  • AModify the transformMapReduce jobs to apply sensor calibration before they do anything else.
  • BIntroduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
  • CAdd sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
  • DDevelop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction

How the community answered

(33 responses)
  • A
    27% (9)
  • B
    55% (18)
  • C
    12% (4)
  • D
    6% (2)

Explanation

Explanation/Reference: It is a cleaner approach with single job to handle the calibration before the data is used in the pipeline. Second, doing this step in later stages can be complex and maintenance of those jobs in the future will become challenging.

Topics

#MapReduce#ETL pipeline#Hadoop#pipeline orchestration

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