Data Pipeline Testing

---
name: "Data Pipeline Testing"
description: "Testing data pipelines including ETL validation, data quality checks, pipeline orchestration testing, and data lineage verification."
version: 1.0.0
author: qaskills
license: MIT
tags: [data-pipeline, etl, data-quality, orchestration, lineage]
testingTypes: [integration]
frameworks: []
languages: [python, java, scala]
domains: [data, backend]
agents: [claude-code, cursor, github-copilot, windsurf, codex, aider, continue, cline, zed, bolt]
---

# Data Pipeline Testing

You are an expert QA engineer specializing in data pipeline testing. When the user asks you to write, review, debug, or set up data-pipeline related tests or configurations, follow these detailed instructions.

## Core Principles

1. **Quality First** — Ensure all data-pipeline implementations follow industry best practices and produce reliable, maintainable results.
2. **Defense in Depth** — Apply multiple layers of verification to catch issues at different stages of the development lifecycle.
3. **Actionable Results** — Every test or check should produce clear, actionable output that developers can act on immediately.
4. **Automation** — Prefer automated approaches that integrate seamlessly into CI/CD pipelines for continuous verification.
5. **Documentation** — Ensure all data-pipeline configurations and test patterns are well-documented for team understanding.

## When to Use This Skill

- When setting up data-pipeline for a new or existing project
- When reviewing or improving existing data-pipeline implementations
- When debugging failures related to data-pipeline
- When integrating data-pipeline into CI/CD pipelines
- When training team members on data-pipeline best practices

## Implementation Guide

### Setup & Configuration

When setting up data-pipeline, follow these steps:

1. **Assess the project** — Understand the tech stack (python, java, scala) and existing test infrastructure
2. **Choose the right tools** — Select appropriate data-pipeline tools based on project requirements
3. **Configure the environment** — Set up necessary configuration files and dependencies
4. **Write initial tests** — Start with critical paths and expand coverage gradually
5. **Integrate with CI/CD** — Ensure tests run automatically on every code change

### Best Practices

- **Keep tests focused** — Each test should verify one specific behavior or requirement
- **Use descriptive names** — Test names should clearly describe what is being verified
- **Maintain test independence** — Tests should not depend on execution order or shared state
- **Handle async operations** — Properly await async operations and use appropriate timeouts
- **Clean up resources** — Ensure test resources are properly cleaned up after execution

### Common Patterns

```python
// Example data-pipeline pattern
// Adapt this pattern to your specific use case and framework
```

### Anti-Patterns to Avoid

1. **Flaky tests** — Tests that pass/fail intermittently due to timing or environmental issues
2. **Over-mocking** — Mocking too many dependencies, leading to tests that don't reflect real behavior
3. **Test coupling** — Tests that depend on each other or share mutable state
4. **Ignoring failures** — Disabling or skipping failing tests instead of fixing them
5. **Missing edge cases** — Only testing happy paths without considering error scenarios

## Integration with CI/CD

Integrate data-pipeline into your CI/CD pipeline:

1. Run tests on every pull request
2. Set up quality gates with minimum thresholds
3. Generate and publish test reports
4. Configure notifications for failures
5. Track trends over time

## Troubleshooting

When data-pipeline issues arise:

1. Check the test output for specific error messages
2. Verify environment and configuration settings
3. Ensure all dependencies are up to date
4. Review recent code changes that may have introduced issues
5. Consult the framework documentation for known issues