Overview

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.

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