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"item_title" : "Python Data Engineering",
"item_author" : [" Roman Estavelle "],
"item_description" : "Ever had a data pipeline break at 2 a.m., silently drop half your data, and still claim everything is green? Yeah-this book is for that. Python Data Engineering is a practical, no-fluff guide to building reliable, scalable, and production-ready data systems using Python. It's written for engineers who are tired of fragile pipelines, mystery failures, and dashboards that lie with a straight face. This book walks you step by step through the entire data engineering lifecycle-from ingestion to transformation, orchestration, analytics, and production hardening-without pretending that everything works perfectly the first time (it doesn't). You'll learn how to: Design bulletproof batch and streaming pipelinesWrite production-grade Python that survives real-world dataHandle schema drift, late data, retries, and backfills without panicBuild clean ELT/ETL architectures that scale with your businessImplement data quality checks, testing, and observabilityOptimize performance without burning moneyDeploy, monitor, and evolve data systems that won't wake you up at nightAlong the way, we'll cover modern data engineering concepts like workflow orchestration, analytics engineering, semantic layers, testing strategies, real-time processing, security, governance, and incident response-explained clearly, practically, and with a sense of humor.This is not a hello world Python book.It's a how-to-survive-in-production book.If you are: A Python developer moving into data engineeringA data engineer who wants stronger fundamentalsAn analytics engineer who wants to understand the full stackOr someone who just wants pipelines that don't explode...this book is written for you.No magic. No hype. Just solid systems, real lessons, and a clear path to building data platforms you can actually trust. Let's build pipelines that work-even when things go wrong.",
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Python Data Engineering : Build Bulletproof Pipelines & Analytics Systems
Overview
Ever had a data pipeline break at 2 a.m., silently drop half your data, and still claim everything is "green"? Yeah-this book is for that.
Python Data Engineering is a practical, no-fluff guide to building reliable, scalable, and production-ready data systems using Python. It's written for engineers who are tired of fragile pipelines, mystery failures, and dashboards that lie with a straight face. This book walks you step by step through the entire data engineering lifecycle-from ingestion to transformation, orchestration, analytics, and production hardening-without pretending that everything works perfectly the first time (it doesn't). You'll learn how to:- Design bulletproof batch and streaming pipelines
- Write production-grade Python that survives real-world data
- Handle schema drift, late data, retries, and backfills without panic
- Build clean ELT/ETL architectures that scale with your business
- Implement data quality checks, testing, and observability
- Optimize performance without burning money
- Deploy, monitor, and evolve data systems that won't wake you up at night
Along the way, we'll cover modern data engineering concepts like workflow orchestration, analytics engineering, semantic layers, testing strategies, real-time processing, security, governance, and incident response-explained clearly, practically, and with a sense of humor.
- This is not a "hello world" Python book.
- It's a how-to-survive-in-production book.
If you are:
- A Python developer moving into data engineering
- A data engineer who wants stronger fundamentals
- An analytics engineer who wants to understand the full stack
- Or someone who just wants pipelines that don't explode
- ...this book is written for you.
No magic. No hype. Just solid systems, real lessons, and a clear path to building data platforms you can actually trust. Let's build pipelines that work-even when things go wrong.
This item is Non-Returnable
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Details
- ISBN-13: 9798185164198
- ISBN-10: 9798185164198
- Publisher: Independently Published
- Publish Date: July 2026
- Dimensions: 11 x 8.5 x 0.72 inches
- Shipping Weight: 1.78 pounds
- Page Count: 348
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