Data engineering projects python von mondal masud (15 Ergebnisse)

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

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

Paperback. Zustand: New.

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

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

Taschenbuch. Zustand: 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.…

Taschenbuch. Zustand: 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.…

Paperback. Zustand: New.

Paperback. Zustand: 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.…

Zustand: New. Print on Demand.

Paperback. Zustand: New.

Paperback. Zustand: new. Paperback. 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 SQLProcess large datasets with Apache Spark and PySparkDesign real-time streaming systems using Apache KafkaOrchestrate complex workflows with Apache AirflowImplement cloud-native architectures using AWS S3, Glue, Redshift, and LambdaBuild distributed analytics warehouses with performance-optimized table designCreate Bronze, Silver, and Gold Medallion data lakehouse architecturesImplement Change Data Capture and incremental loading strategiesMonitor pipeline health with logging, metrics, and automated alertingBuild automated data quality validation frameworks and production quality gatesOptimize distributed systems for scalability, performance, and cloud cost efficiencyDesign 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Zustand: New. Print on Demand.

Paperback. Zustand: 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Zustand: new. Paperback. 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 SQLProcess large datasets with Apache Spark and PySparkDesign real-time streaming systems using Apache KafkaOrchestrate complex workflows with Apache AirflowImplement cloud-native architectures using AWS S3, Glue, Redshift, and LambdaBuild distributed analytics warehouses with performance-optimized table designCreate Bronze, Silver, and Gold Medallion data lakehouse architecturesImplement Change Data Capture and incremental loading strategiesMonitor pipeline health with logging, metrics, and automated alertingBuild automated data quality validation frameworks and production quality gatesOptimize distributed systems for scalability, performance, and cloud cost efficiencyDesign 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…