Reason About Duplicate Data and Scaling in Spark
Company: Cognitiv
Role: Software Engineer
Category: Software Engineering Fundamentals
Difficulty: hard
Interview Round: Technical Screen
Overview: Explain how to define and remove duplicate data in Spark, then scale the pipeline as volume and traffic increase. Candidates must translate business identity into deterministic batch or streaming operations while addressing shuffles, skew, state retention, late data, retries, idempotent sinks, backfills, tuning, and observability.
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