Data Engineering Projects with Python & SQL : Build Real ETL Pipelines, Data Warehouses & Analytics Systems (Beginner to Job-Ready, 2026)
Language: English
Published by Independently Published Apr 2026, 2026
Series: Book 2 of 15 - Data Engineering
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 18.57
Quantity: 2 available
Add to basketItem description from seller
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. …
Seller Inventory # 9798258941459
- Title
- Data Engineering Projects with Python & SQL : Build Real ETL Pipelines, Data Warehouses & Analytics Systems (Beginner to Job-Ready, 2026)
- Author
- Masud Mondal
- Publisher
- Independently Published Apr 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798258941459
- Item weight
- 188 grams
- Dimensions
- 229x152x7 mm
- Series
- Book 2 of 15: Data Engineering
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 Build
Working 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 Reading
Every 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-Ready
This 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 For
This 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 Different
No 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 Path
Start 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.
"Synopsis" may belong to another edition of this title.
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