About this book:

"Data Engineering for Programmers" offers a comprehensive guide for developers looking to master the art of building robust, scalable, and reliable data pipelines. This book demystifies the entire data life cycle, from initial ingestion and meticulous transformation to intelligent storage and efficient consumption. Readers will learn to navigate the complexities of modern data systems, exploring crucial concepts such as designing for reliability and resilience, handling schema evolution and versioning, and ensuring data quality through validation and rigorous testing.

Beyond the core mechanics of ETL and ELT, the book delves into advanced topics essential for production-grade data workflows. It covers various data ingestion patterns—batch, streaming, and micro-batch—alongside best practices for interacting with diverse source systems and APIs, including the powerful capabilities of Change Data Capture (CDC). The guide then explores the expansive world of data storage, comparing relational, NoSQL, data lake, and lakehouse architectures, and optimizes these systems with strategies for partitioning, compression, and lifecycle management, all while introducing the latest open file formats like Parquet, ORC, Delta Lake, and Apache Iceberg.

Crucially, the book extends beyond technical implementation, emphasizing the operational and strategic aspects of data engineering. It equips programmers with the knowledge to build and manage scalable pipelines, deploy workflows using containers and cloud orchestration (Kubernetes), and implement comprehensive monitoring, logging, and data observability. Furthermore, it addresses critical non-technical pillars like data governance, privacy, security, compliance, cross-team data contracts, and cost optimization. The final chapter focuses on future-proofing data architectures, encouraging a mindset of continuous learning and adaptability to navigate the ever-evolving data landscape.

What You'll Find Inside:

• Master the core principles of data engineering, including roles, mindset, and the complete data life cycle from ingestion to consumption.
• Learn to design and implement reliable data pipelines with various ingestion patterns (batch, streaming, micro-batch) and robust error handling.
• Explore diverse data storage solutions, from traditional relational and NoSQL databases to modern data lakes and lakehouse architectures utilizing open file formats like Parquet and ORC.
• Gain practical skills in data transformation strategies (ETL vs. ELT), building modular and testable pipelines, and ensuring high data quality through validation and error handling.
• Understand crucial operational and strategic aspects like schema evolution, idempotency, pipeline scalability, cloud deployment, monitoring, data governance, privacy, security, cost optimization, and future-proofing architectures.

Who's It For:

This book is for programmers and software engineers looking to transition into or deepen their understanding of data engineering. It specifically targets those who want to build robust, scalable, and reliable data pipelines and storage systems in production environments, emphasizing best practices for ingestion, transformation, storage, deployment, and operational management in cloud-native settings.