**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
spark-submit: `spark-submit --class com.Main --master yarn --deploy-mode cluster --executor-memory 4G app.jar arg1`. For PySpark: `spark-submit script.py`. Options: `--packages`, `--conf`, `--py-files`....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like LTIMindtree. The answer also includes follow-up discussion points that interviewers commonly explore.
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