**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Tech stack justification: Spark—in-memory, fast for iterative/ML; supports batch and streaming. Hadoop—mature, HDFS for storage. Cloud (AWS/GCP/Azure)—managed services, elasticity, less ops. Choose based on: data size, latency requirements, team skills, cost. Example: Real-time → Spark Streaming + Kafka. Batch analytics → Spark on EMR/Databricks....
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 KPMG. The answer also includes follow-up discussion points that interviewers commonly explore.
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