validate-pipeline

To run an end-to-end smoke test on an ETL data pipeline, execute a validation process that checks each stage—from loading raw data to processed outputs—runs without errors and meets schema expectations. It verifies loading, cleaning, feature engineering, and formatting steps.

Data pipeline schema drift occurs when incoming data structure changes unexpectedly. Validation catches it by checking for consistency across pipeline stages, ensuring outputs meet expectations, and documenting schema failures with actionable details during the smoke test.

Yes, you can use automated validation for data pipelines with standard ETL layouts. It targets projects structured with src/, data/raw/, and data/processed/ directories, checking loading, cleaning, feature engineering, and formatting steps for reproducibility.

The best way to detect missing data across ETL pipeline stages is running a full smoke test that ensures cross-stage consistency. It validates each stage, checks for missing data, and documents failures with actionable details for early detection.

Your data pipeline smoke test fails during feature engineering due to broken imports, schema drift, or integration errors. The validation process targets this stage specifically, documenting the exact failure with actionable details to fix formatting or consistency issues.