Modern ETL Testing with AI: SQL, Python & AI for Real-World Data Validation (QA Testing, Band 4) - Softcover

Inhaltsangabe

Modern ETL Testing with AI
SQL, Python & AI for Real-World Data Validation

Modern ETL Testing Series — Volume 1

Want to build practical ETL testing skills using SQL, Python, and AI?

Whether you're starting your ETL testing journey, moving from manual QA into data testing, or looking to automate repetitive validation tasks, Modern ETL Testing with AI — Volume 1 provides a practical path from fundamentals to real-world ETL validation.

Rather than focusing only on theory, this book explains how ETL testers approach common data-quality problems: validating source and target data, identifying missing or duplicate records, verifying transformations, reconciling datasets, automating checks with Python, and using AI as a testing assistant.

ETL Testing Fundamentals

• ETL architecture and data flow
• Source-to-target validation
• Data quality and transformation testing
• Common ETL testing scenarios

SQL for ETL Testing

• SELECT, WHERE, GROUP BY and ORDER BY
• INNER, LEFT, RIGHT and FULL OUTER JOINs
• Aggregations and reconciliation
• NULL and duplicate validation
• Transformation and business-rule validation
• Source-to-target comparisons

Python for ETL Testing

• pandas-based data validation
• Database connectivity
• Automated comparison scripts
• Validation functions and PASS/FAIL reporting

Data Warehouse Testing

• Fact and dimension tables
• Star schema
• Slowly Changing Dimensions (SCD)
• Historical data validation

Real-World ETL Validation

• Row-count and NULL validation
• Duplicate detection
• Aggregate validation
• Data-type and format checks
• Transformation validation
• Source-to-target reconciliation
• Incremental and business-rule validation

AI-Assisted ETL Testing

• Generate SQL validation queries
• Create Python testing scripts
• Generate test cases
• Investigate validation failures
• Use AI responsibly while validating its output

The book treats AI as an assistant and productivity accelerator—not a replacement for testing judgment.

Modern Data & Cloud Environments

Understand how ETL testing fits into modern data architectures, cloud data platforms, orchestration, and data pipelines.

Apply the concepts through two realistic ETL testing projects:

• Banking ETL Testing Project
• Insurance ETL Testing Project

• Beginners starting a career in ETL Testing
• QA Engineers moving into Data Testing
• Manual Testers learning automation
• Automation Testers exploring ETL and Data Quality
• Junior Data Engineers
• Freshers preparing for ETL Testing interviews
• Professionals interested in AI-assisted testing

Modern ETL Testing with AI — Volume 1 is the foundation volume of the Modern ETL Testing Series. It builds the core skills required for practical ETL testing before moving into advanced cloud, big-data, streaming, enterprise, observability, CI/CD, and AI-assisted testing topics in later volumes.

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