convention-data-validation

Data quality and schema validation are enforced across analytics pipelines by applying Pydantic for input validation and Great Expectations for dataset checks to surface errors early. This solution integrates validation conventions directly into ingestion and processing stages.

Validating ETL inputs is best handled by applying Pydantic models to API and function parameters to catch type and structure errors early. This ensures downstream consistency by preventing invalid data from entering your processing pipeline.

Data quality metrics and error handling reports are generated by executing a Great Expectations validation suite on loaded raw data. This process applies schema validation rules and produces a quality report surfacing any dataset issues.

Yes, Pydantic and Great Expectations can be used together for end-to-end data validation by splitting responsibilities across the pipeline. Pydantic handles initial input validation, while Great Expectations drives the subsequent dataset checks and quality reporting.

Schema validation catches data structure issues during the ingestion stage by enforcing expected formats and types before data reaches processing. This early validation maintains input integrity and prevents bad data from polluting analytics.

Limitations of using validation conventions include the need to explicitly define Pydantic models and Great Expectations suites upfront for every new dataset. Without these predefined schema structures, the pipeline cannot automatically infer or validate data quality metrics.