Python Data Engineering in Action: Construct ETL Pipelines, Process Large Datasets, Automate Workflows, and Build Production-Ready Data Systems

By Howley Cahill

Python Data Engineering in Action is a complete, practical, and modern guide to building production-ready data systems using Python. Whether you're a beginner stepping into data engineering for the first time or a working developer looking to strengthen your pipeline skills, this book gives you everything you need to extract, process, transform, validate, and deploy data reliably at scale.You'll learn how to design end-to-end ETL and ELT pipelines, automate workflows, manage structured and unstructured data, work with streaming sources, optimize performance, and deliver systems that run smoothly in real production environments. Each chapter moves from concept to application, offering detailed explanations, real-world examples, and hands-on Python code you can use immediately.This book does not just teach techniques - it teaches you how to think like a production data engineer. You'll understand how to make trade-offs between batch and streaming systems, structure your transformations, enforce data quality, handle schema changes safely, design robust monitoring, and deploy pipelines confidently using containers and cloud orchestration tools.You will explore essential topics such as: Working with large datasets efficiently using Python, Pandas, Polars, Dask, Ray, and PySparkExtracting data from APIs, files, logs, databases, cloud buckets, and streaming sourcesCleaning, validating, standardizing, and transforming data for analytics and productionWriting scalable pipelines with reusable components and automated testsPerforming incremental loading, partitioning, compaction, and idempotent writesOperating modern data architectures including data lakes, lakehouses, warehouses, and distributed processing systemsDeploying pipelines with Docker, CI/CD, Kubernetes, ECS, and serverless platformsBuilding real-time pipelines with Kafka and message brokersImplementing observability with structured logging, metrics, alerts, and troubleshooting workflowsDesigning hybrid batch/streaming architectures and maintaining them long-termEvery concept is explained clearly so you can use it immediately, and each chapter includes insights drawn from real production systems. By the end of this book, you'll know how to build data platforms that are dependable, well-structured, easy to extend, and ready for the scale and complexity of modern data workloads.Who This Book Is ForAspiring data engineersSoftware developers expanding into data engineeringPython engineers interested in ETL, streaming, or distributed systemsAnalysts transitioning to pipeline developmentStudents and professionals preparing for data engineering rolesTeams who want to design consistent, reliable data systemsNo prior experience with distributed computing or cloud platforms is required. The book guides you carefully from simple foundations to advanced, production-grade patterns.Why This Book Stands OutUnlike many resources that only cover theory or isolated examples, this book gives you a complete and practical path from extraction to deployment. You will gain: Real production patternsAccurate and authentic coding examplesReusable templates and checklistsTroubleshooting guidanceDeployment-ready workflowsClear explanations without unnecessary jargonIf you want to build data pipelines that work reliably - not just in controlled examples but in actual production environments - this book is your blueprint.Call to ActionReady to build real data systems that solve real problems? Take the next step in your career and transform the way you handle data.

• Format: Paperback
• Language: English
• ISBN: B0G3534M93
• ISBN13: 9798275327595
• Release Date: November 2025
• Publisher: Independently Published
• Length: 490 Pages
• Weight: 1.85 lbs.
• Dimensions: 1.0" x 7.0" x 10.0"