**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
PySpark on-paper challenge: Read data, identify columns, state transformations (filter, join, agg), write pseudocode or actual PySpark. Example: Given id, amount, date—compute daily sum per id. `df.groupBy('id','date').sum('amount')`....
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 Paytm. The answer also includes follow-up discussion points that interviewers commonly explore.
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