MODERN DATA ENGINEERING WITH PYTHON Building Reliable ETL Pipelines, Automated Workflows, and High-Performance Big Data

MODERN DATA ENGINEERING WITH PYTHON: Building Reliable ETL Pipelines, Automated Workflows, and High-Performance Big Data Systems (The CodeCraft Series)
English | December 19, 2025 | ASIN: B0G99WJNR6 | 243 pages | Epub | 308.41 KB
Unlock the Power of Modern Data Engineering with Python. Transform raw data into actionable insights and scalable systems with practical, hands-on guidance. Dive into the world of modern data engineering as this book takes you step by step through building reliable ETL pipelines, automating workflows, and handling big data efficiently using Python. From foundational concepts to advanced techniques, you'll learn how to clean, transform, and validate data, orchestrate workflows, implement fault-tolerant pipelines, and scale processing for enterprise-grade systems. Real-world examples, mini-projects, and detailed Python implementations make complex concepts easy to understand and apply. Whether you're a developer, data analyst, or aspiring data engineer, this book equips you with the skills to build end-to-end data systems. Learn how to optimize performance, maintain data quality, and implement observability to ensure your data pipelines are trustworthy and production-ready. By combining theoretical principles with practical, hands-on exercises, you'll gain confidence in designing, deploying, and maintaining robust data engineering solutions. What readers will gain from this book: Mastery of ETL and ELT pipeline design for modern data systems. Practical Python skills for automating data ingestion, transformation, and loading. Techniques to ensure pipeline reliability, fault tolerance, and observability. Strategies for handling big data and scalable distributed processing. Best practices for testing, monitoring, and productionizing data pipelines. Start building modern, reliable, and scalable data engineering systems today, take control of your data and transform it into business value with Python.