Design a Fault-tolerant Data Transformation Pipeline Using Pandas

Instruction: Describe how you would design and implement a fault-tolerant pipeline for cleaning and transforming data using Pandas, ensuring that the pipeline can handle errors gracefully without data loss.

Context: Design a reliable Pandas transformation pipeline with explicit validation, durable checkpoints, idempotent retries and verifiable publication.

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Pandas performs transformations; durable recovery and publication must be designed around it.

Keep an immutable source/version identifier and transformation version. Validate schema, key uniqueness, ranges and expected row counts before publishing. Separate expected bad records from infrastructure or programming failures. Quarantine invalid records with protected provenance and an explicit failure/partial-result policy. Do not silently skip them and report success...

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