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Python For Data Engineering: Building Reliable Pipelines from Extraction to Production-Grade Warehouses
Paperback
Series: The Applied Python Library
Publisher: Independently Published
Published: Jul 13 2026
Pages: 224
Weight: 0.87
Height: 0.47 Width: 7.00 Depth: 10.00
Language: English
Behind every dashboard, machine learning model, business report, or real-time analytics platform lies a data pipeline responsible for collecting, validating, transforming, and delivering trustworthy data. Building those pipelines requires far more than writing Python scripts, it requires engineering systems that are reliable, scalable, and capable of running unattended in production.
Python for Data Engineering is a practical guide for Python developers who want to master the discipline of modern data engineering. Rather than focusing on isolated code examples, this book teaches you how to design complete data pipelines the way professional engineering teams build them: one reliable component at a time.
Throughout the book, you'll build Conduit, a production-grade data pipeline that evolves chapter after chapter. Starting with structured data formats and database connectivity, you'll progressively develop a complete system capable of extracting data from multiple sources, validating data quality, transforming datasets efficiently, orchestrating workflows, monitoring pipeline health, and loading information into modern data warehouses.
Unlike many books that concentrate only on ETL code, this guide emphasizes one of the most overlooked realities of data engineering: silent failures. A pipeline that finishes successfully can still produce incorrect data. Learning how to detect, prevent, and monitor these failures is one of the core engineering skills you'll develop throughout the book.
Inside you'll learn how to:
- Build reliable ETL and ELT pipelines using Python
- Read and process CSV, JSON, Parquet, APIs, and databases
- Design robust data validation and quality checks
- Automate incremental loading and Change Data Capture (CDC)
- Work with SQL, SQLAlchemy, and modern database workflows
- Process large datasets efficiently using PySpark
- Design dimensional models and production-ready data warehouses
- Organize scalable Data Lakes using modern storage formats
- Test, monitor, and deploy production-grade data pipelines
- Build complete end-to-end engineering projects from extraction to analytics
Every chapter combines clear explanations, practical Python code, engineering best practices, and realistic business scenarios. Instead of memorizing isolated techniques, you'll understand why production pipelines fail, how experienced engineers prevent data corruption, and what separates experimental scripts from systems businesses can trust.
Whether you're preparing for a career in data engineering, expanding your Python expertise, or transitioning from data analysis into large-scale data infrastructure, this book provides the practical knowledge needed to build reliable data systems with confidence.
By the end of the journey, you won't simply know how to manipulate data.
You'll know how to engineer the pipelines that deliver it.
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