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Practical Data Quality Engineering
Detect Duplicates, Missing Values, Invalid Records, Schema Changes, and Silent Data Errors
Alemão
54 b
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The pipeline is green, every job reports success, and the number on the dashboard is still wrong. If you build or maintain tables that other people trust, this book shows you how to catch the problems that do not announce themselves. You will profile a table before writing rules, find exact and near duplicates, tell harmless gaps from harmful ones, write layered rules for invalid records, spot schema changes before they spread, and expose silent errors with volume, freshness and reconciliation checks. Every technique comes with runnable SQL or Python and pandas examples. The final chapters turn the checks into automated tests, a quarantine path for bad rows, monitoring that alerts sparingly, and a calm routine for the day something breaks.
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