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Data Engineering in Practice: Pipelines, ETL, and RealTime Streams
Paperback
Publisher: Independently Published
Published: Jun 29 2025
Pages: 246
Weight: 0.74
Height: 0.52 Width: 6.00 Depth: 9.00
Language: English
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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