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Modern ETL Testing with AI: SQL, Python & AI for Real-World Data Validation (QA Testing) - Softcover
Book 1 of 6: QA Testing
Synopsis
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.
What You'll LearnETL 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.
Practical ProjectsApply 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.
"synopsis" may belong to another edition of this title.
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Modern ETL Testing with AI: SQL, Python & AI for Real-World Data Validation (QA Testing)
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Modern ETL Testing with AI
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Modern ETL Testing with AI (Paperback)
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Paperback. Condition: new. Paperback. Modern ETL Testing with AISQL, Python & AI for Real-World Data ValidationModern ETL Testing Series - Volume 1Are you looking to start a career in ETL Testing or upgrade your skills with SQL, Python, and AI?Do you find traditional ETL books too theoretical and difficult to follow?Modern ETL Testing with AI - Volume 1 is a beginner-friendly, practical guide designed to help you learn ETL testing step by step using simple language, real-world examples, and hands-on projects.Instead of overwhelming you with theory, this book focuses on what ETL testers actually do in real projects-validating data, writing SQL queries, automating checks with Python, and using AI tools to work faster and smarter. What You'll LearnUnderstand ETL architecture and data flow from source systems to data warehousesMaster SQL fundamentals for ETL testing, including: SELECT, WHERE, GROUP BY, ORDER BYINNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOINAggregations and reconciliation queriesPractical data validation techniquesLearn Python for ETL testing: pandas basicsDatabase connectivityAutomated validation scriptsPASS/FAIL reportingExplore Data Warehouse concepts: Fact and Dimension tablesStar SchemaSlowly Changing Dimensions (SCD)Perform common ETL validations: Row count validationNull value validationDuplicate record checksAggregate validationSource-to-target reconciliationDiscover how AI can assist ETL testers: Generate SQL queriesCreate Python scriptsWrite test casesAnalyze validation failuresUse ChatGPT, Claude, and Gemini effectivelyUnderstand modern Cloud ETL architecture and industry trendsBuild confidence with two real-world projects: Banking ETL Testing ProjectInsurance ETL Testing ProjectWhy This Book Is DifferentUnlike many ETL books that focus heavily on concepts, this book emphasizes: Simple explanations with minimal jargonPractical SQL and Python examplesAI-assisted testing techniquesReal enterprise scenariosInterview-oriented insightsBeginner to intermediate learning pathWho Should Read This Book?This book is ideal for: Beginners starting their ETL Testing journeyQA Engineers transitioning to Data TestingManual Testers learning automationAutomation Testers exploring ETL and Data QualityFreshers preparing for ETL Testing interviewsProfessionals interested in AI-powered testingModern ETL Testing SeriesThis book is Volume 1 of the Modern ETL Testing Series, a practical series that teaches ETL testing using modern technologies and real-world examples.Upcoming volumes cover: Advanced ETL Testing with AICloud ETL Testing with AIPython Automation for ETL TestingCDC, SCD & Incremental Load TestingSnowflake, Databricks & Modern Data Pipelines This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798181826137
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Modern ETL Testing with AI : SQL, Python & AI for Real-World Data Validation
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Taschenbuch. Condition: Neu. Neuware - Modern ETL Testing with AISQL, Python & AI for Real-World Data ValidationModern ETL Testing Series - Volume 1Are you looking to start a career in ETL Testing or upgrade your skills with SQL, Python, and AI Do you find traditional ETL books too theoretical and difficult to follow Modern ETL Testing with AI - Volume 1 is a beginner-friendly, practical guide designed to help you learn ETL testing step by step using simple language, real-world examples, and hands-on projects.Instead of overwhelming you with theory, this book focuses on what ETL testers actually do in real projects-validating data, writing SQL queries, automating checks with Python, and using AI tools to work faster and smarter. What You'll LearnUnderstand ETL architecture and data flow from source systems to data warehousesMaster SQL fundamentals for ETL testing, including: - SELECT, WHERE, GROUP BY, ORDER BY- INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN- Aggregations and reconciliation queries- Practical data validation techniquesLearn Python for ETL testing: - pandas basics- Database connectivity- Automated validation scripts- PASS/FAIL reportingExplore Data Warehouse concepts: - Fact and Dimension tables- Star Schema- Slowly Changing Dimensions (SCD)Perform common ETL validations: - Row count validation- Null value validation- Duplicate record checks- Aggregate validation- Source-to-target reconciliationDiscover how AI can assist ETL testers: - Generate SQL queries- Create Python scripts- Write test cases- Analyze validation failures- Use ChatGPT, Claude, and Gemini effectivelyUnderstand modern Cloud ETL architecture and industry trendsBuild confidence with two real-world projects: - Banking ETL Testing Project- Insurance ETL Testing ProjectWhy This Book Is Different- Unlike many ETL books that focus heavily on concepts, this book emphasizes: - Simple explanations with minimal jargon- Practical SQL and Python examples- AI-assisted testing techniques- Real enterprise scenarios- Interview-oriented insights- Beginner to intermediate learning pathWho Should Read This Book This book is ideal for: - Beginners starting their ETL Testing journey- QA Engineers transitioning to Data Testing- Manual Testers learning automation- Automation Testers exploring ETL and Data Quality- Freshers preparing for ETL Testing interviews- Professionals interested in AI-powered testingModern ETL Testing SeriesThis book is Volume 1 of the Modern ETL Testing Series, a practical series that teaches ETL testing using modern technologies and real-world examples.Upcoming volumes cover: - Advanced ETL Testing with AI- Cloud ETL Testing with AI- Python Automation for ETL Testing- CDC, SCD & Incremental Load Testing- Snowflake, Databricks & Modern Data Pipelines. Seller Inventory # 9798181826137
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