Data Engineering in Practice Pipelines, ETL, and RealTime Streams

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.

• 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.