Advanced Data Engineering Projects with Python, SQL & Cloud: Build Scalable Pipelines, Big Data Systems & Production Platforms
Language: English
Published by Independently published, 2026
Series: Book 4 of 15 - Data Engineering
- Softcover
- New
Seller: California Books, Miami, FL, U.S.A.California Books
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- Title
- Advanced Data Engineering Projects with Python, SQL & Cloud: Build Scalable Pipelines, Big Data Systems & Production Platforms
- Author
- Mondal, Masud
- Publisher
- Independently published
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798196379000
- Series
- Book 4 of 15: Data Engineering
Build production-grade data engineering systems used in modern cloud environments — and build them alongside a complete companion GitHub repository.
Companion GitHub Repository: Follow the projects in this book with the complete source code, sample datasets, configuration files, tests, documentation, and implementation resources for all 16 hands-on projects:
https://github.com/masud-dot/advanced-data-engineering-projects
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, tested, monitored, and operated.
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 project 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 deployment
Who 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, optimize 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.
"Synopsis" may belong to another edition of this title.
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