What frameworks does this skill support?

It integrates with Pydantic for record-level validation, Pandera for DataFrame schemas, and Great Expectations for pipeline monitoring and data warehouse checks.

Can I use this for custom business rules?

Yes, the skill includes patterns for custom business rules through validators like date range checks, value set validation, and referential integrity functions.

Is this suitable for production ETL tasks?

Yes, it is specifically designed for data engineers building production-grade validation pipelines that require monitoring and strict schema enforcement.

How do I implement a configuration-driven pipeline?

You can use the 'run_validation_pipeline' function which accepts a configuration dictionary defining unique columns, date ranges, and other constraints.

Does it support automated testing?

Absolutely. It includes a suite of test assertions (e.g., assert_schema_match, assert_no_nulls) designed to be used within frameworks like pytest.

Data Validation & Pipeline Helpers

Name: Data Validation & Pipeline Helpers
Author: majesticlabs-dev

bymajesticlabs-dev

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Data Science & ML

Standardizes data quality across ETL pipelines with custom validation patterns, schema evolution, and automated test assertions.

About

The Data Validation skill empowers data engineers and developers to enforce rigorous data quality standards within their Python-based workflows. It provides a comprehensive suite of tools for record-level validation using Pydantic, DataFrame schema enforcement via Pandera, and pipeline monitoring through Great Expectations. Whether you're building config-driven ETL pipelines, checking referential integrity between datasets, or implementing unit tests for data quality, this skill streamlines the process of ensuring data integrity and catching schema drift early.

Key Features

Multi-framework support for Pydantic, Pandera, and Great Expectations integration
Config-driven validation pipelines for automated data cleaning workflows
Helpers for managing schema evolution and versioning across datasets
18 GitHub stars
Custom validation patterns for complex business rules like referential integrity
Standardized test assertions for rigorous data quality monitoring in CI/CD

Use Cases

Validating Pandas DataFrames against strict schema and type requirements
Automating data quality checks for data warehouse ingestion pipelines
Ensuring data integrity in record-by-record ETL processing tasks

About

Key Features

Multi-framework support for Pydantic, Pandera, and Great Expectations integration
Config-driven validation pipelines for automated data cleaning workflows
Helpers for managing schema evolution and versioning across datasets
18 GitHub stars
Custom validation patterns for complex business rules like referential integrity
Standardized test assertions for rigorous data quality monitoring in CI/CD

Use Cases

Validating Pandas DataFrames against strict schema and type requirements
Automating data quality checks for data warehouse ingestion pipelines
Ensuring data integrity in record-by-record ETL processing tasks