**Section 1 — The Context (The 'Why')**
ETL pipelines combining Kafka and Spark Streaming must reconcile batch-oriented processing with continuous ingestion. The primary challenge: offset management—without checkpoints, a Spark job restart replays from the beginning or skips data. Another failure mode is duplicate writes from at-least-once delivery when the sink is not idempotent....
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 Meesho. The answer also includes follow-up discussion points that interviewers commonly explore.
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