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Anotace knihy

Are you still writing one-off scripts instead of building real data pipelines?

Most Python developers struggle to transition into data engineering because they lack one thing - production-level design skills.

This book changes that.

Advanced Python Patterns for Data Engineering is a practical, real-world guide to building scalable, reliable, and production-ready data systems using Python.

Instead of focusing on theory, this book teaches you how real data engineers design pipelines that run daily, handle failures, and scale to millions of records.

What You'll Learn

• How to design reusable ETL and ELT pipelines

• Advanced Python patterns for scalable data workflows

• Incremental loading, idempotency, and schema evolution

• Error handling, retry strategies, and circuit breakers

• Data validation, monitoring, and observability

• Parallel and distributed processing concepts

• Partitioning, caching, and performance optimization

• Batch vs streaming architecture

• How to build production-ready pipelines from scratch

Real-World Focus

This book includes:

• real production case studies

• architecture-level thinking

• complete end-to-end project

• industry best practices

Who This Book Is For

• Python developers moving into data engineering

• aspiring data engineers

• data analysts upgrading to pipeline development

• engineers preparing for interviews

• professionals who want to build production systems

What Makes This Book Different

Unlike most books, this is not about writing scripts.

It's about:
designing systems
handling real-world failures
building scalable data platforms

Parametry knihy

• Plný název: Advanced Python Patterns for Data Engineering
• Podnázev: Build Production-Ready ETL Pipelines, Scalable Data Systems, and Reliable Data Platforms
• Autor: Abhishek Kumar
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 156
• EAN: 9798252666761
• ID: 51499490
• Nakladatelství: Independently published
• Hmotnost: 219 g
• Rozměry: 229 × 152 × 8 mm
• Datum vydání: 18. March 2026

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