data-engineering

Idempotent pipeline design ensures reruns produce identical output via upsert or date-range overwrite. Schema validation at ingestion catches messy inputs by logging failures, while deduplication on business keys prevents duplicate records from corrupting downstream analytics.

Schema validation at ingestion uses Pydantic to validate CSV inputs and log failures explicitly. Converting validated CSV data to Parquet format enables efficient analytics, allowing DuckDB to query Parquet files directly without loading them entirely into memory.

DuckDB can query Parquet files that exceed available memory by processing data in batches without loading entire files. This makes it suitable for analytics workflows where large Parquet datasets need filtering, aggregation, or joining without full memory consumption.

Handle schema drift and validation errors by applying schema validation at ingestion using Pydantic to log failures rather than silently dropping records. Drift detection identifies evolving schemas, while verification gates validate row counts against explicit expectations during recurring pipeline reruns.

Deduplicate records by business key by applying explicit deduplication rules during the transformation stage. Verification gates then check resulting row counts against expectations, ensuring duplicates are removed and output remains stable and consistent across pipeline reruns.

Idempotency and quality gates are needed when pipelines run on recurring schedules or process messy inputs. Idempotency ensures identical output across reruns, while quality gates verify row counts and enforce deduplication, preventing silent data corruption in analytics workflows.