PySpark for Data Testing Automation — Lesson 1: PySpark Foundations for Testing

Learning Objectives

• Create Spark sessions and work with DataFrames, schemas, columns, and expressions
• Read and write common file and table formats
• Use transformations, actions, filters, joins, aggregations, and windows

Detailed Topics

• Create Spark sessions and work with DataFrames: Create Spark sessions and work with DataFrames, schemas, columns, and expressions
• Read and write common file and table formats: Read and write common file and table formats
• Use transformations: Use transformations, actions, filters, joins, aggregations, and windows

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

Build a PySpark notebook that profiles and summarizes a large dataset.

• 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 PySpark Foundations for Testing in a realistic scenario and validate the result with documented evidence.