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Published by CreateSpace Independent Publishing Platform, 2026
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Paperback. Condition: new. Paperback. Modern ETL Testing with AISQL, Python & AI for Real-World Data ValidationModern ETL Testing Series - Volume 1Want 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 FundamentalsETL architecture and data flowSource-to-target validationData quality and transformation testingCommon ETL testing scenariosSQL for ETL TestingSELECT, WHERE, GROUP BY and ORDER BYINNER, LEFT, RIGHT and FULL OUTER JOINsAggregations and reconciliationNULL and duplicate validationTransformation and business-rule validationSource-to-target comparisonsPython for ETL Testingpandas-based data validationDatabase connectivityAutomated comparison scriptsValidation functions and PASS/FAIL reportingData Warehouse TestingFact and dimension tablesStar schemaSlowly Changing Dimensions (SCD)Historical data validationReal-World ETL ValidationRow-count and NULL validationDuplicate detectionAggregate validationData-type and format checksTransformation validationSource-to-target reconciliationIncremental and business-rule validationAI-Assisted ETL TestingGenerate SQL validation queriesCreate Python testing scriptsGenerate test casesInvestigate validation failuresUse AI responsibly while validating its outputThe book treats AI as an assistant and productivity accelerator-not a replacement for testing judgment.Modern Data & Cloud EnvironmentsUnderstand 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 ProjectInsurance ETL Testing ProjectWho Is This Book For?Beginners starting a career in ETL TestingQA Engineers moving into Data TestingManual Testers learning automationAutomation Testers exploring ETL and Data QualityJunior Data EngineersFreshers preparing for ETL Testing interviewsProfessionals interested in AI-assisted testingModern ETL Testing SeriesModern 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condition: new. Paperback. Take Your ETL Testing Skills to the Cloud, at Scale, and into the Age of AIModern ETL Testing with AI - Volume 2 picks up where foundational ETL testing leaves off. If you can already validate a row count, compare source and target, and write a solid Python assertion, this book takes you the… rest of the way: into cloud data warehouses, data lakes, Spark, Kafka, CDC pipelines, enterprise CI/CD, and the growing role of AI agents in building and maintaining test coverage.Written for testers, data engineers, and QA professionals now responsible for pipelines spanning multiple clouds, near-real-time streams, and audit-grade reporting.What's InsideCloud Data Platforms: Test Snowflake, Redshift, BigQuery, and Synapse for clustering drift, materialized view staleness, and cost-aware validation using zero-copy cloning and time travel.Data Lakes & Storage: Catch the small-file problem, validate Delta Lake/Iceberg/Hudi schema enforcement, and test object storage access control.Orchestration: Build three-layer test suites for Airflow DAGs, catch trigger-rule misconfigurations, and test Glue, Data Factory, and Dataflow pipelines.Big Data & Streaming: Diagnose data skew in Spark, test Kafka delivery guarantees and idempotent consumers, and validate CDC pipelines for snapshot gaps, tombstones, and out-of-order events.Enterprise Practices: Manage test data with masking and synthetic generation, enforce data contracts, build observability, design CI/CD with canary deployments, and performance-test at scale.AI-Powered Testing: Use AI agents to generate test coverage across hundreds of tables with mandatory human review, apply LLM-assisted triage to correlate anomalies, and build a governed, audited AI-augmented framework.Real Enterprise Case Studies: Three full case studies-a multi-cloud retail platform, a real-time banking fraud detection system, and a HIPAA-governed healthcare data lake-show how these techniques combine under real business and regulatory pressure.Interview Preparation: A large bank of interview questions across beginner through lead/architecture levels, covering cloud, Spark, Kafka, Snowflake, dbt, Airflow, CI/CD, and AI-specific topics, plus four complete mock interview transcripts with evaluator commentary.Why This Book Is DifferentEvery chapter follows the same discipline: explain why the problem matters before showing how to solve it, ground every technique in runnable SQL, Python, PySpark, and YAML code, and close with testing checkpoints you can use as a QA checklist. Real-world examples throughout-a skewed join that broke a telecom billing pipeline, a silent delete gap in an e-commerce inventory feed, a currency conversion bug caught by a canary deployment-show how these techniques catch real, costly defects before they reach production.The AI chapters take a disciplined approach: agents accelerate test generation and anomaly triage, but every consequential decision stays under human review, with clear guardrails against AI quietly locking in bugs.Who This Book Is ForETL testers and QA engineers moving from foundational to cloud-scale, enterprise-scale testingData engineers building reliable, well-tested pipelines on AWS, Azure, or GCPSenior and lead engineers preparing for cloud, big data, or architecture-level interviewsTeams evaluating how to responsibly adopt AI-assisted testing and observabilityWhether you're preparing for your next interview, building a testing practice for a growing data platform, or leading a team through the shift to cloud-native, AI-augmented data engineering, this volume gives you practical, tested, immediately usable techniques to get there. T Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Condition: new. Paperback. Master API Automation Testing with Python - and Become Job-ReadyAre you ready to move beyond manual testing and build automation skills that companies are actively hiring for? Do you want to write real API tests, build a professional framework, and walk into interviews with something concret…e to show?This book is your complete, practical guide to making that happen.API Automation Testing with Python: Requests, PyTest, and AI-Powered Testing takes you from foundational understanding to real-world application - step by step, without assuming prior automation experience. Every concept is grounded in what you will actually do on the job. What You Will Learn?Write API tests using Python, Requests, and PyTestTest GET, POST, PUT, and DELETE operations with full validationHandle authentication flows and perform security testingBuild a complete, professional API automation frameworkIntegrate your test suite into a CI/CD pipelineUse AI tools to generate test cases and improve efficiencyPrepare confidently for QA and automation interviewsA Real Project, Built by YouThis book includes a complete end-to-end API testing project - structured the way professional teams build theirs, with real endpoints, CRUD operations, CI/CD integration, and reporting. Push it to GitHub and present it in any interview with confidence. AI-Powered Testing - Built Into the CurriculumYou will learn to use AI to generate test scenarios, write automation code faster, and diagnose failures more efficiently - while developing the critical judgment to validate everything AI produces.AI is not replacing testers. It is raising the bar for what skilled testers can accomplish. This book shows you how to clear it.Who This Book Is For?Beginners entering QA, manual testers transitioning to automation, students preparing for IT careers, and professionals adding Python automation to their skill set. No prior automation experience required.Start building your skills. Start building your career. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Independently Published Apr 2026, 2026
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Taschenbuch. Condition: Neu. Neuware - Master API Automation Testing with Python - and Become Job-ReadyAre you ready to move beyond manual testing and build automation skills that companies are actively hiring for Do you want to write real API tests, build a professional framework, and walk into interviews with something concret…e to show This book is your complete, practical guide to making that happen.API Automation Testing with Python: Requests, PyTest, and AI-Powered Testing takes you from foundational understanding to real-world application - step by step, without assuming prior automation experience. Every concept is grounded in what you will actually do on the job. What You Will Learn - Write API tests using Python, Requests, and PyTest- Test GET, POST, PUT, and DELETE operations with full validation- Handle authentication flows and perform security testing- Build a complete, professional API automation framework- Integrate your test suite into a CI/CD pipeline- Use AI tools to generate test cases and improve efficiency- Prepare confidently for QA and automation interviewsA Real Project, Built by You>AI-Powered Testing - Built Into the CurriculumYou will learn to use AI to generate test scenarios, write automation code faster, and diagnose failures more efficiently - while developing the critical judgment to validate everything AI produces.AI is not replacing testers. It is raising the bar for what skilled testers can accomplish. This book shows you how to clear it.Who This Book Is For Beginners entering QA, manual testers transitioning to automation, students preparing for IT careers, and professionals adding Python automation to their skill set. No prior automation experience required.Start building your skills. Start building your career.
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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 T…esting 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.
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Paperback. Condition: new. Paperback. Master API Automation Testing with Python - and Become Job-ReadyAre you ready to move beyond manual testing and build automation skills that companies are actively hiring for? Do you want to write real API tests, build a professional framework, and walk into interviews with something concret…e to show?This book is your complete, practical guide to making that happen.API Automation Testing with Python: Requests, PyTest, and AI-Powered Testing takes you from foundational understanding to real-world application - step by step, without assuming prior automation experience. Every concept is grounded in what you will actually do on the job. What You Will Learn?Write API tests using Python, Requests, and PyTestTest GET, POST, PUT, and DELETE operations with full validationHandle authentication flows and perform security testingBuild a complete, professional API automation frameworkIntegrate your test suite into a CI/CD pipelineUse AI tools to generate test cases and improve efficiencyPrepare confidently for QA and automation interviewsA Real Project, Built by YouThis book includes a complete end-to-end API testing project - structured the way professional teams build theirs, with real endpoints, CRUD operations, CI/CD integration, and reporting. Push it to GitHub and present it in any interview with confidence. AI-Powered Testing - Built Into the CurriculumYou will learn to use AI to generate test scenarios, write automation code faster, and diagnose failures more efficiently - while developing the critical judgment to validate everything AI produces.AI is not replacing testers. It is raising the bar for what skilled testers can accomplish. This book shows you how to clear it.Who This Book Is For?Beginners entering QA, manual testers transitioning to automation, students preparing for IT careers, and professionals adding Python automation to their skill set. No prior automation experience required.Start building your skills. Start building your career. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
