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Python for Data Engineering: Build Scalable Pipelines, ETL Systems, and Automate Data Workflows
Paperback
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ISBN13: 9798293914760
Publisher: Independently Published
Published: Jul 23 2025
Pages: 230
Weight: 0.89
Height: 0.48 Width: 7.00 Depth: 10.00
Language: English
Publisher: Independently Published
Published: Jul 23 2025
Pages: 230
Weight: 0.89
Height: 0.48 Width: 7.00 Depth: 10.00
Language: English
Python for Data Engineering: Build Scalable Pipelines, ETL Systems, and Automate Data Workflows Python for Data Engineering is a hands-on, practical guide for building reliable and scalable data systems using Python. Whether you're wrangling datasets, designing ETL pipelines, or automating workflows, this book walks you through every stage of the data engineering lifecycle. From data ingestion and transformation to workflow orchestration and cloud deployment, it equips you with the tools and best practices needed to build production-grade data infrastructure.
Designed for both aspiring and experienced data engineers, this book focuses on real-world implementation, covering modern tools such as Apache Airflow, Pandas, Docker, and cloud platforms like AWS and GCP. You'll learn how to process large volumes of data, schedule complex workflows, manage dependencies, and deliver high-quality data pipelines that scale.
Master the core skills of modern data engineering using Python. This book starts with fundamental concepts such as working with files, APIs, and databases and gradually moves toward advanced topics like parallel processing, CI/CD for data pipelines, and deploying to the cloud. Each chapter combines theory with step-by-step projects that demonstrate how to solve real engineering problems. Along the way, you'll learn how to debug workflows, document your pipelines, ensure reproducibility, and collaborate effectively in teams. Key Features of This Book
Master the core skills of modern data engineering using Python. This book starts with fundamental concepts such as working with files, APIs, and databases and gradually moves toward advanced topics like parallel processing, CI/CD for data pipelines, and deploying to the cloud. Each chapter combines theory with step-by-step projects that demonstrate how to solve real engineering problems. Along the way, you'll learn how to debug workflows, document your pipelines, ensure reproducibility, and collaborate effectively in teams. Key Features of This Book
- Build end-to-end ETL and ELT pipelines using Python and SQL
- Automate data workflows using Apache Airflow and scheduling tools
- Connect to APIs, work with cloud storage, and handle large datasets efficiently
- Implement CI/CD workflows with GitHub Actions for pipeline automation
- Deploy data solutions on AWS and Google Cloud
- Follow best practices for version control, testing, documentation, and reproducibility
- Includes templates, reusable code snippets, and sample configurations
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