How do you handle schema changes in source systems that could break your data pipeline? What strategies prevent and mitigate these failures?

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Source schema changes are one of the most common causes of data pipeline failures. A renamed column, a changed data type, or a new table structure can break extraction, transformation, and loading processes, leaving dashboards without fresh data.