Data Engineering in Practice

Build Data Systems That Scale—From ETL to Real-Time Streaming

The modern world runs on data. But collecting it is only the beginning. Data Engineering in Practice is your hands-on guide to designing and building reliable, scalable data pipelines—from batch ETL to real-time stream processing.

This book is perfect for aspiring data engineers, software developers, and analytics professionals who want to go beyond theory and start building production-grade data infrastructure.

You’ll learn how to choose the right tools, architect efficient pipelines, and ensure your data flows cleanly from source to storage to insight—all with performance and reliability in mind.

Inside You’ll Learn:

• The role of the data engineer in modern analytics and AI stacks

• How to build robust ETL and ELT pipelines

• Real-time stream processing with tools like Apache Kafka and Spark Streaming

• Orchestrating workflows using Apache Airflow

• Working with structured and unstructured data at scale

• Data lake vs. data warehouse: when to use what

• Scaling pipelines with cloud-native tools (AWS, GCP, Azure)

• Ensuring data quality, observability, and monitoring

• Best practices for automation, versioning, and reproducibility

Whether you're building your first pipeline or scaling a streaming platform to millions of events per minute, this book will help you do it right—from Day 1.

Power your data. Architect the flow. Engineer for scale.