Title: Modern Data Engineering with SQL and Python : Build Production-Grade ETL Pipelines, Analytics Work-flows, Orchestration Systems, and Scalable Data Platforms
Publisher: Independently Published Mai 2026
Publication Date: 2026
Language: English
ISBN 13: 9798197322449
Binding: Taschenbuch
Condition: Neu
Dimensions: 279 millimeters width by 216 millimeters height by 24 millimeters depth
Weight: 1059 grams

About this title

Synopsis

Modern data engineering is no longer about writing a few Python scripts or running isolated SQL queries.

Learn how to build production-grade ETL pipelines, orchestration workflows, scalable data platforms, and enterprise analytics systems using SQL and Python.

Today’s organizations depend on:

scalable data pipelines,
analytics engineering workflows,
warehouse architectures,
and modern data platforms

\
to power reporting, automation, forecasting, operational intelligence, and business decision-making reliably.

But many aspiring data engineers feel trapped between:

fragmented tutorials,
disconnected tools,
shallow toy projects,
and beginner content that never explains how real production systems actually operate.

Knowing SQL alone is not enough.

Knowing Python alone is not enough.

Modern production data engineering requires understanding how:

ETL and ELT workflows,
orchestration systems,
transformation pipelines,
warehouse architectures,
observability systems,
semantic reporting layers,
and enterprise analytics workflows

all work together inside scalable operational ecosystems.

That is exactly what this book teaches.

Instead of focusing on isolated tools, Modern Data Engineering with SQL and Python helps you develop the systems-thinking mindset used by professional data engineers, analytics engineers, and modern data platform architects.

Inside this book, you will learn how to:

Build production-grade ETL pipelines with SQL and Python
Design scalable data pipelines and modern data platforms
Engineer reliable orchestration workflows and scheduling systems
Create maintainable analytics engineering transformation layers
Structure enterprise-ready data warehouse architecture systems
Develop validation, monitoring, and observability workflows
Optimize large-scale warehouse transformations and reporting pipelines
Coordinate multi-system enterprise analytics workflows professionally
Handle schema drift, retries, replay recovery, and pipeline failures safely
Think like a production systems engineer instead of a tutorial-driven tool user

Unlike many beginner-focused books, this guide emphasizes:

operational reliability,
scalability,
maintainability,
observability,
workflow coordination,
semantic consistency,
and long-term production engineering discipline.

Throughout the book, you’ll follow a continuous enterprise retail and logistics case study that demonstrates how modern production data engineering systems behave under real operational conditions.

You’ll learn not just how pipelines execute — but how professional engineers design systems that remain:

scalable,
recoverable,
observable,
governable,
and trustworthy

as organizational complexity grows.

Whether you want to become a:

data engineer,
analytics engineer,
BI engineer,
ETL developer,
or modern data platform professional,

this book gives you the practical engineering foundation most tutorials never teach.

If you’re ready to move beyond isolated scripts and start building real production-grade data systems, scroll up and grab your copy today.

"About this title" may belong to another edition of this title.

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