Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Process large CSV in chunks with pandas: import pandas as pd; chunks = pd.read_csv('large.csv', chunksize=10000); seen = set(); results = []; [results.append(chunk.drop_duplicates(subset=['email','timestamp'], keep='last')) for chunk in chunks if not...
This hard-level SQL question appears frequently in data engineering interviews at companies like Amazon. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (python) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Process large CSV in chunks with pandas: import pandas as pd; chunks = pd.read_csv('large.csv', chunksize=10000); seen = set(); results = []; [results.append(chunk.drop_duplicates(subset=['email','timestamp'], keep='last')) for chunk in chunks if not (chunk[['email','timestamp']].apply(tuple, axis=1).isin(seen).all())] — but cross-chunk duplicates remain. Better: read chunks, concatenate, then drop_duplicates: dfs = [chunk for chunk in pd.read_csv('file.csv', chunksize=50000)]; df = pd.concat(dfs).drop_duplicates(subset=['email','timestamp'], keep='last'). For true streaming dedup: use a hash set and process chunk-by-chunk, only keeping rows where (email, ts) not in seen, then add to seen. Why it matters: Design choices compound at scale—wrong approach can cause 100× overhead. Scalability trade-offs: Profile before optimizing; validate on sample then full. Cost implications: Suboptimal choices multiply at billion-row scale.
Red Flag: Assuming pipeline success means data correctness. Pro-Move: 'We added row checksums and reconciliation—caught 0.02% drift that success status missed.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.