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Paperback. Condizione: 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. This item is p Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Taschenbuch. Condizione: Neu. Neuware - 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 Fundamentals- ETL architecture and data flow- Source-to-target validation- Data quality and transformation testing- Common ETL testing scenariosSQL 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 comparisonsPython for ETL Testing- pandas-based data validation- Database connectivity- Automated comparison scripts- Validation functions and PASS/FAIL reportingData Warehouse Testing- Fact and dimension tables- Star schema- Slowly Changing Dimensions (SCD)- Historical data validationReal-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 validationAI-Assisted ETL Testing- Generate SQL validation queries- Create Python testing scripts- Generate test cases- Investigate validation failures- Use 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 Project- Insurance ETL Testing ProjectWho Is This Book For - 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 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.…

Taschenbuch. Condizione: Neu. Neuware - Stop Learning. Start Building. Become Job-Ready in Data Engineering.Data engineering is one of the fastest-growing careers in tech - but most resources teach theory, not real-world skills. This book is different.Instead of long explanations, you will build 12 complete, real-world data engineering projects using Python and SQL. Every project simulates a genuine business scenario, follows a professional structure, and gives you something tangible to show employers.What You Will BuildWorking through this book, you will create end-to-end data pipelines including CSV and file-based ingestion systems, API data pipelines with error handling, data cleaning and validation workflows, JSON to SQL transformation pipelines, a star schema data warehouse, reporting data marts for analytics, e-commerce and log processing pipelines, incremental loading systems, fault-tolerant and reliable pipelines, performance-optimized data workflows, and automated testing for data quality.Learn by Doing - Not Just ReadingEvery project follows the same professional structure: business problem, pipeline architecture, step-by-step implementation, working code, expected output, resume-ready bullet points, and interview questions. You won't just understand the concepts - you will know how to apply them.Become Job-ReadyThis book is designed to help you build a strong GitHub portfolio, write powerful resume project points, answer real data engineering interview questions with confidence, and develop the end-to-end thinking that employers are looking for. By the last page, you will have practical, demonstrable experience that sets you apart from other candidates.Who This Book Is ForThis book is written for beginners starting a career in data engineering, developers and software engineers transitioning into data roles, QA and automation engineers looking to upskill, and anyone who learns best through hands-on, project-based work. Basic knowledge of Python and SQL is all you need to get started.Why This Book Is DifferentNo unnecessary theory. No long academic explanations. No abstract concepts disconnected from practice. Only real projects, practical learning, and job-focused skills built through doing.Your Learning PathStart with simple file-based pipelines. Progress through API ingestion, data warehousing, and analytics systems. Finish with production-ready patterns including incremental loading, fault tolerance, performance optimization, and automated testing.By the End of This Book, You Will Have: Built 12 real-world data engineering projects. Developed end-to-end pipeline thinking. Created a job-ready portfolio. And gained the confidence to apply for data engineering roles - not someday, but now.…

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Taschenbuch. Condizione: 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 concrete 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.…

Paperback. Condizione: New.

Taschenbuch. Condizione: Neu. Neuware - Master Apache Kafka the way working data engineers use it - with Python, real projects, and the judgement to match.Every modern data platform runs on event streams, and Kafka sits at the centre of nearly all of them. Yet most engineers meet Kafka through a copied tutorial and a producer that quietly loses messages. This book closes the gap between running a hello-world example and operating a reliable pipeline in production.A complete path, from first broker to full pipelines: - Part 1 - Kafka Fundamentals: architecture, KRaft mode, installation with Docker Compose, topics, partitions, offsets, and your first Python producers and consumers- Part 2 - Working with Kafka: CLI tools, Avro and Schema Registry, consumer groups in depth, replication and fault tolerance, exactly-once semantics, and performance tuning- Part 3 - Kafka for Data Engineers: streaming ETL, Kafka Connect, production-grade Python application structure, data quality and dead letter queues, real-time analytics, monitoring with Prometheus and Grafana, and security- Part 4 - Real Projects: four complete, runnable systems - an order processing pipeline, clickstream analytics with windowing, an IoT sensor pipeline, and a Debezium CDC pipelineBuilt for interviews as well as production. The final chapter delivers over 100 interview questions with model answers across architecture, reliability, ecosystem, security, scenario design, and behavioural rounds - plus three timed mock interviews and a career roadmap.Inside every chapter: - Runnable Python code using the confluent-kafka client- Architecture diagrams, comparison tables, and worked sizing examples- Best practices, common mistakes, and troubleshooting guidance drawn from production experience- Interview tips, practice exercises, and a five-question quiz with answersWho this book is for: beginner and junior data engineers, ETL developers, analysts moving into data engineering, software engineers adopting event-driven systems, students, and anyone preparing for data engineering interviews where Kafka appears in the job description - which today means nearly all of them.No prior Kafka experience is required. If you can write basic Python and run a Docker command, this book will take you the rest of the way - from your first topic to a monitored, secured, production-shaped pipeline you built yourself.…

Taschenbuch. Condizione: Neu. Neuware - Build production-grade data engineering systems used in modern cloud environments - not just beginner ETL scripts.As organizations continue shifting toward cloud-native analytics, real-time processing, and distributed data platforms, the demand for skilled data engineers has never been higher. Yet most learning resources still focus on isolated concepts rather than showing how production systems are actually designed, built, and operated at scale.This book changes that.Advanced Data Engineering Projects with Python, SQL & Cloud is a hands-on, project-based guide that takes you beyond the basics and develops the practical engineering skills that modern data engineering roles demand. Every chapter is built around realistic production scenarios - not toy examples - using technologies widely adopted across the industry.What you will learn - Build scalable ETL and ELT pipelines using Python and SQL- Process large datasets with Apache Spark and PySpark- Design real-time streaming systems using Apache Kafka- Orchestrate complex workflows with Apache Airflow- Implement cloud-native architectures using AWS S3, Glue, Redshift, and Lambda- Build distributed analytics warehouses with performance-optimized table design- Create Bronze, Silver, and Gold Medallion data lakehouse architectures- Implement Change Data Capture and incremental loading strategies- Monitor pipeline health with logging, metrics, and automated alerting- Build automated data quality validation frameworks and production quality gates- Optimize distributed systems for scalability, performance, and cloud cost efficiency- Design complete enterprise-level data platforms from requirements to deploymentWho this book is for This book is written for aspiring data engineers building their first serious portfolio, ETL developers transitioning into cloud and big data platforms, backend engineers exploring distributed data systems, analytics engineers deepening their technical foundation, and professionals preparing for mid-level or senior data engineering interviews.Why this book is different Most technical books teach tools in isolation. This book teaches how modern systems work together - and more importantly, how to think like an engineer who builds them.You will learn not only how to use these technologies, but how to design for scalability, build for reliability, optimise for performance, and solve the kinds of problems that appear in real production environments at 2 a.m. when something breaks.By the end of this book, you will have a collection of advanced portfolio-quality projects, a strong foundation in enterprise architecture patterns, interview preparation material covering system design and production troubleshooting, and the practical engineering mindset that separates strong candidates in today's competitive data engineering job market.…

Taschenbuch. Condizione: Neu. Neuware.

Taschenbuch. Condizione: Neu. Neuware - Walk into your data engineering interview ready for every round.Most people don't fail data engineering interviews because they lack skill. They fail because no one showed them what each round is really testing, or how to answer under pressure. This book fixes that. Data Engineering Interview Mastery & System Design is a complete, end-to-end preparation guide that follows the real shape of a modern DE interview, from SQL and Python fundamentals to the senior system design rounds where the best offers are won and lost. What you'll master: - SQL interview problems: joins, window functions, CTEs, indexing, and query optimization- Python for data engineers: generators, decorators, OOP, concurrency, and 'what does this print ' questions- Warehousing & modeling: OLTP vs OLAP, star and snowflake schemas, slowly changing dimensions, and Data Vault- Lakes & lakehouses: Delta/Iceberg, ACID on files, schema evolution, and the medallion architecture- Big data & cloud: Spark internals, shuffles and data skew, Kafka delivery guarantees, AWS, and Airflow- System design: a repeatable framework plus four full designs - Batch ETL, Real-Time Streaming, Data Lakehouse, and a 5-billion-events/day Clickstream platform- Career mastery: resume, LinkedIn, portfolio projects, mock interviews, and salary negotiationWhy this book is different : Every key question follows a proven 8-part format - Question, Why Interviewers Ask, Simple Answer, Detailed Explanation, Real Business Example, Common Mistakes, Follow-up Questions, and Difficulty - so you don't just memorize an answer, you understand it and handle the follow-ups that actually decide the outcome. You also get 285+ rapid-fire questions in the appendices (Top 100 SQL, Top 50 Python, Top 50 Spark, Top 30 Kafka, Top 30 AWS, Top 25 System Design) and quick-reference cheat sheets for last-minute revision the night before. Who it's for : Freshers and career switchers, ETL/QA/analyst professionals moving into data engineering, software engineers crossing over, and experienced engineers targeting senior roles. Nobody is talked down to, and nothing is padded. This isn't a list of facts to memorize. It teaches you to think the way a data engineer thinks: clarify the problem, reason about how systems behave and fail, weigh the trade-offs, and say it all clearly out loud. The work ahead is real, and it's learnable. Scroll up, grab your copy, and start preparing the way the engineers who get hired do.…

Paperback. Condizione: 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.…

Condizione: New. Print on Demand.

Paperback. Condizione: 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.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. Neuware - AUTOMATION TESTING WITH PYTHON & SELENIUM - SECOND EDITION Build practical automation skills with Python and Selenium and progress from your first browser test to a maintainable, enterprise-style automation framework. Automation Testing with Python & Selenium - Second Edition is a hands-on, project-driven guide for students, aspiring automation testers, manual QA professionals transitioning to automation, and working QA engineers who want stronger framework and engineering skills. Instead of teaching Selenium as a collection of isolated scripts, this book follows a structured learning path. You will learn the fundamentals, build reusable automation components, and progressively evolve them into a professional framework using PyTest, Page Object Model, configuration management, test data, logging, reporting, Git, CI/CD, Docker, API testing, and AI-assisted testing techniques. Throughout the book, you will work with a realistic GlobalShop QA project that brings the concepts together into one continuous automation journey. WHAT YOU WILL LEARN- Python programming fundamentals for automation testers- Selenium WebDriver and reliable browser automation- Locators, waits, synchronization, debugging, and test stability- PyTest fixtures, test organization, and scalable framework design- Page Object Model and maintainable automation architecture- Configuration management and environment-aware testing- Data-driven testing using practical test data- Logging, screenshots, and professional test reporting- Git, branching, collaboration, and version-controlled automation- CI/CD pipelines and automated test execution- Docker and scalable test execution- Selenium Grid and cloud-based execution concepts- API testing and hybrid UI + API testing- AI-assisted techniques for smarter test automation- A realistic end-to-end GlobalShop QA automation project>LEARN BY BUILDING>WHO THIS BOOK IS FOR- Students learning software test automation- Beginners starting with Python and Selenium- Manual testers transitioning into automation- Junior and intermediate QA automation engineers- Experienced QA engineers and SDETs improving framework skills- Developers interested in test automation>WHY THIS SECOND EDITION >By the end of the book, you will have a clearer understanding of how professional Python + Selenium automation frameworks are designed, developed, tested, maintained, and integrated into modern software delivery workflows. COMPANION RESOURCES>Build the skills. Build the framework. Build the confidence to work with modern test automation.…

Taschenbuch. Condizione: Neu. Neuware - Crack Azure Data Engineer Interviews with Real Questions, Real Scenarios, and Real AnswersAzure Data Engineer interviews have changed. Panels no longer stop at definitions - they probe how you designed a pipeline, why you chose one Azure service over another, how you recovered a failed load at 2 a.m., and what your architecture costs the business every month. Azure Data Engineer Interview Mastery prepares you for exactly that kind of interview.What's Inside- 600+ interview questions - detailed worked answers, chapter quizzes, and a 300-question rapid revision pack- 100 scenario-based questions covering real production failure modes across twelve categories- 55+ comparison tables, 20+ architecture diagrams, and 35+ SQL, Python, and PySpark code examples- Five complete enterprise project walkthroughs - banking, retail, healthcare, e-commerce, and IoT- A full mock interview, HR and behavioural round preparation, and a rapid revision chapter for the night before your interviewFull Coverage of the Modern Azure Data StackAzure fundamentals and career roadmap - Azure Storage and ADLS Gen2 - Azure Data Factory (fundamentals and real project patterns) - Synapse Analytics architecture and performance tuning - Azure Databricks and Apache Spark - Microsoft Fabric and OneLake - SQL for data engineers (window functions, CTEs, MERGE, indexing, execution plans) - Python for data engineers - ETL and data modelling concepts (SCDs, star schemas, medallion architecture) - Cloud architecture patterns (Lambda, Kappa, data mesh, event-driven design) - Azure DevOps and CI/CD - Security and governance - Performance and cost optimisation.DP-203 Concepts ThroughoutEvery relevant chapter flags DP-203 exam-aligned concepts, so this book doubles as a certification companion alongside your interview preparation.Who This Book Is For- Freshers preparing for their first Azure Data Engineer interview- Data engineers, ETL developers, and BI developers moving to Azure- Cloud engineers and SQL developers broadening into data platforms- DP-203 learners who want interview-ready depth- Working professionals with up to ten years of experience targeting product companies, consultancies, and large enterprisesWritten in clear, practical, professional English by an author who has built production Azure data platforms across banking, retail, healthcare, e-commerce, and IoT - this is the most complete Azure Data Engineer interview book available today.Prepare once. Walk into every round with confidence. …

Taschenbuch. Condizione: Neu. Neuware.

Taschenbuch. Condizione: Neu. Neuware - SQL for Data Engineering - ETL, Warehousing, Cloud Platforms & AI WorkflowsMost SQL books teach you to write queries. This one teaches you to build systems.If you know basic SQL but struggle to apply it in production environments, or you are preparing for data engineering interviews and want experience beyond simple SELECT statements, this book was written for you.SQL for Data Engineering bridges the gap between tutorial SQL and the skills that data engineering roles actually demand - ETL pipelines, warehouse design, query optimization, and the architectural thinking that separates engineers who build reliable data systems from those who only query them.What you will learn: - Advanced querying - joins, CTEs, subqueries, window functions, and analytical SQL patterns- Data transformation - cleaning, standardisation, safe casting, and deduplication techniques- ETL architecture - incremental loading, staging patterns, Slowly Changing Dimensions, and idempotent pipeline design- Warehouse design - star schemas, dimensional modelling, partitioning, indexing, and materialised views- Performance optimisation - EXPLAIN ANALYZE, execution plans, query tuning, and production-grade optimisation- Semi-structured data - JSON and JSONB processing, schema drift handling, and event pipeline patterns- Cloud platforms - Snowflake, BigQuery, Redshift, and Databricks with practical SQL for each- AI workflows - prompt engineering for SQL tasks, AI-assisted debugging, and the risks of generated codeFour industry projects you will build: - E-Commerce Analytics Warehouse- SaaS Subscription Metrics Platform- Banking Transaction Fraud Monitoring System- Food Delivery ETL and Operations PipelineEach project covers a realistic business problem, complete ETL pipeline, warehouse schema, KPI queries, and interview preparation - designed to go directly into your portfolio.You also get: - Integrated interview Q&A in every chapter drawn from real hiring panels- Resume bullet points after every chapter and project- A complete career roadmap for aspiring data engineers- Appendices covering SQL functions, data types, troubleshooting, glossary, and a tool landscape guide.…

Taschenbuch. Condizione: Neu. Neuware.

Paperback. Condizione: new. Paperback. Stop Learning. Start Building. Become Job-Ready in Data Engineering.Data engineering is one of the fastest-growing careers in tech - but most resources teach theory, not real-world skills. This book is different.Instead of long explanations, you will build 12 complete, real-world data engineering projects using Python and SQL. Every project simulates a genuine business scenario, follows a professional structure, and gives you something tangible to show employers.What You Will BuildWorking through this book, you will create end-to-end data pipelines including CSV and file-based ingestion systems, API data pipelines with error handling, data cleaning and validation workflows, JSON to SQL transformation pipelines, a star schema data warehouse, reporting data marts for analytics, e-commerce and log processing pipelines, incremental loading systems, fault-tolerant and reliable pipelines, performance-optimized data workflows, and automated testing for data quality.Learn by Doing - Not Just ReadingEvery project follows the same professional structure: business problem, pipeline architecture, step-by-step implementation, working code, expected output, resume-ready bullet points, and interview questions. You won't just understand the concepts - you will know how to apply them.Become Job-ReadyThis book is designed to help you build a strong GitHub portfolio, write powerful resume project points, answer real data engineering interview questions with confidence, and develop the end-to-end thinking that employers are looking for. By the last page, you will have practical, demonstrable experience that sets you apart from other candidates.Who This Book Is ForThis book is written for beginners starting a career in data engineering, developers and software engineers transitioning into data roles, QA and automation engineers looking to upskill, and anyone who learns best through hands-on, project-based work. Basic knowledge of Python and SQL is all you need to get started.Why This Book Is DifferentNo unnecessary theory. No long academic explanations. No abstract concepts disconnected from practice. Only real projects, practical learning, and job-focused skills built through doing.Your Learning PathStart with simple file-based pipelines. Progress through API ingestion, data warehousing, and analytics systems. Finish with production-ready patterns including incremental loading, fault tolerance, performance optimization, and automated testing.By the End of This Book, You Will Have: Built 12 real-world data engineering projects. Developed end-to-end pipeline thinking. Created a job-ready portfolio. And gained the confidence to apply for data engineering roles - not someday, but now. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.