**Why Unix in Data Eng:** File staging, cleanup, lightweight transforms before Spark. find, awk, sed, xargs, cron—ubiquitous, no runtime deps. **Use Cases:** find /data -name '*.csv' -mtime +7 | xargs gzip. awk -F',' '{print $1,$3}' for column extraction. sed for bulk replace....
This hard-level Python/Coding question appears frequently in data engineering interviews at companies like Comcast. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (python, spark) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Why Unix in Data Eng: File staging, cleanup, lightweight transforms before Spark. find, awk, sed, xargs, cron—ubiquitous, no runtime deps.
Use Cases: find /data -name '.csv' -mtime +7 | xargs gzip. awk -F',' '{print $1,$3}' for column extraction. sed for bulk replace. Cron for scheduling: 0 2 /scripts/ingest.sh.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Python/Coding interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.