PySpark for Data Testing Automation — Lesson 2: Data-Quality Rules and Assertions

Learning Objectives

• Test completeness, uniqueness, validity, consistency, and referential integrity
• Create reusable assertions, rule metadata, severity, and exception handling
• Capture failed records, metrics, evidence, and test summaries

Detailed Topics

• Test completeness: Test completeness, uniqueness, validity, consistency, and referential integrity
• Create reusable assertions: Create reusable assertions, rule metadata, severity, and exception handling
• Capture failed records: Capture failed records, metrics, evidence, and test summaries

Guided Demonstration

1. Review the requirement, intended users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant configuration, analysis, code, application, report, or administration workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, test results, and other useful evidence.
4. Compare the observed result with the acceptance criteria and explain any corrective action.

Hands-On Activity

Implement a configurable data-quality test library.

• Document assumptions, prerequisites, and the planned sequence before starting.
• Complete the activity and preserve evidence of normal, boundary, and failure behavior.
• Record issues, corrective actions, reusable notes, and the next improvement priority.

Scenario Analysis

• Identify the business or technical decision that must be made.
• Compare at least two valid implementation approaches and their trade-offs.
• Consider security, privacy, accessibility, performance, support, and operational impact.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, data, permissions, dependencies, capacity, connectivity, and recent changes.
• Validate rollback, recovery, support ownership, user impact, and business continuity.

Validation Checklist

• The requirement, scope, inputs, and expected outcome are clear.
• The selected design or configuration is documented and follows approved standards.
• Normal, boundary, failure, security, accessibility, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Data-Quality Rules and Assertions in a realistic scenario and validate the result with documented evidence.