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You're working on a real-time machine learning pipeline where records arrive continuously from operational databases and application events. Some records are missing required fields, while others are corrupted, duplicated, or malformed. You need a practical approach for keeping the pipeline usable without letting bad data silently degrade downstream features or predictions.
How would you handle missing or corrupted database records in a real-time machine learning pipeline?
