Can I use this with pandas and SQL databases?

Yes, the skill includes specific templates for pandas DataFrame validation and SQLAlchemy-based integrity checks for databases like PostgreSQL.

What types of ETL tests does this skill cover?

It provides comprehensive patterns for unit tests (transforms), integration tests (full pipelines), data quality checks (SQL-based), and advanced methods like snapshot and property-based testing.

How does this skill handle data quality validation?

It utilizes dbt-style SQL queries within a Pytest framework to verify uniqueness, value constraints, and referential integrity directly against the database.

Does it support property-based testing?

Yes, it includes patterns for using the Hypothesis library to automatically generate test cases for checking mathematical aggregations and data ranges.

ETL Testing Patterns

Name: ETL Testing Patterns
Author: majesticlabs-dev

bymajesticlabs-dev

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Seguridad y Pruebas

Provides Pytest templates and best practices for validating ETL pipelines through unit, integration, and data quality tests.

This skill empowers data engineers to build robust pipelines by providing standardized Pytest patterns for every stage of the ETL lifecycle. It includes specialized templates for validating pandas transformations, ensuring row-level integrity during full-pipeline runs, and executing dbt-style data quality checks against live databases. By incorporating advanced techniques like golden file testing, schema snapshots, and property-based testing with Hypothesis, this skill ensures that your data processing logic remains reliable, idempotent, and production-ready.

Características Principales

01Integration testing patterns to verify row counts and schema preservation.

02Advanced testing methods including Golden File and Snapshot testing.

0319 GitHub stars

04Property-based testing integration for automated edge-case discovery.

05SQL-based data quality checks for uniqueness, constraints, and valid statuses.

06Unit test templates for pandas transformation logic and data cleaning.

Casos de Uso

01Implementing automated regression testing for data migration pipelines.

02Validating complex pandas data cleaning and transformation functions.

03Setting up automated data quality audits within a CI/CD workflow.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add majesticlabs-dev/majestic-marketplace testing-patterns

For use in Claude.ai and ChatGPT

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