Some Data Engineering interview questions look basi

Published 2026-07-09 in Data Engineering

....But one weak answer can expose gaps in production thinking. ~During my interview preparation, I realized that interviewers often ask small questions to check how we handle real-world data issues. For example: 1️⃣ What if a pipeline fails halfway? Approach: • Check logs and failure stage first • Avoid reprocessing already successful data • Use checkpoints or audit tables • Restart from the last successful step 2️⃣ Append vs Overwrite vs Upsert — when to use what? Approach: • Append: when new records are added • Overwrite: when full refresh is required • Upsert: when existing records may change • Always consider data loss and duplicates 3️⃣ How will you handle bad records in a pipeline? Approach: • Validate schema and mandatory fields • Separate valid and invalid records • Move bad records to rejected/discarded path • Log error reason for debugging ~ ♻️ Earlier, I used to think these were simple questions. But now I understand they directly impact data quality, reporting accuracy, and pipeline reliability.

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