**Simple**: s3.upload_file('data.parquet', 'my-bucket', 'path/data.parquet')—handles multipart for >8MB. **From DataFrame**: df.to_parquet('s3://bucket/path/', engine='pyarrow') with s3fs. **Programmatic**: pyarrow read_table, write to BytesIO,...
This easy-level Cloud/Tools question appears frequently in data engineering interviews at companies like Walmart. While less common, it tests deeper understanding that distinguishes strong candidates.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
Simple: s3.upload_file('data.parquet', 'my-bucket', 'path/data.parquet')—handles multipart for >8MB. From DataFrame: df.to_parquet('s3://bucket/path/', engine='pyarrow') with s3fs. Programmatic: pyarrow read_table, write to BytesIO, s3.put_object(Body=buffer.getvalue()). Large files: upload_file does multipart automatically; for >5GB, configure multipart threshold. Encryption: put_object(ServerSideEncryption='aws:kms'). Best practice: Use s3fs with pandas for simple; enable encryption; checksum for integrity.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.