Building an idempotent data pipeline is essential for ensuring data integrity and avoiding duplication, with a critical focus on accurately identifying primary keys. Primary keys, which are unique and relatively immutable fields, enable the identification of unique records and prevent conflicts when records are processed multiple times. In databases, primary keys are typically explicitly declared, simplifying their identification, but challenges arise with changelogs that may not contain full data snapshots. For API endpoints, primary keys are often found in documentation, but when they aren't well-documented, they must be constructed, which can be error-prone due to mutable fields. The lack of well-defined primary keys necessitates reverse-engineering the data model or hashing entire rows, though this approach only prevents duplication from failures, not updates. Ensuring a primary key is always present, either designated by the source or derived, is crucial for maintaining data integrity, especially as the complexity and number of data sources increase.