PySpark for Data Testing Automation

Course Description

SkillDynamo’s PySpark for Data Testing Automation course develops practical, role-relevant capability in automating scalable data-quality, reconciliation, and pipeline testing with PySpark and Microsoft-oriented data platforms. The learning path moves from essential concepts and design decisions to implementation, validation, troubleshooting, and operational use.

Learners work through realistic scenarios, guided demonstrations, applied exercises, and evidence-based reviews. Each topic is connected to workplace responsibilities, common risks, quality checks, and measurable business outcomes.

The course supports professionals who need to build dependable skills, explain their decisions clearly, identify failure points, and create an actionable improvement plan for future projects.

What You Will Learn

• Use Spark sessions, DataFrames, schemas, transformations, and actions
• Profile large datasets for completeness, validity, uniqueness, and consistency
• Create reusable PySpark validation and assertion functions
• Test joins, aggregations, transformations, and business rules
• Compare source and target datasets using reconciliation techniques
• Handle nulls, duplicates, schema drift, and malformed records
• Integrate data tests with notebooks, pipelines, and CI workflows
• Diagnose performance, partitioning, dependency, and test failures

SkillDynamo Training Approach

SkillDynamo delivers this course through instructor-led demonstrations, hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, solution reviews, and a final improvement plan. Learners repeatedly practice planning, implementation, evidence collection, testing, correction, and communication so the skills can be transferred confidently into real workplace situations.