Synopsis
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.
"synopsis" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. Build Data Systems That Scale-From ETL to Real-Time StreamingThe 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 stacksHow to build robust ETL and ELT pipelinesReal-time stream processing with tools like Apache Kafka and Spark StreamingOrchestrating workflows using Apache AirflowWorking with structured and unstructured data at scaleData lake vs. data warehouse: when to use whatScaling pipelines with cloud-native tools (AWS, GCP, Azure)Ensuring data quality, observability, and monitoringBest practices for automation, versioning, and reproducibilityWhether 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798290199948
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Data Engineering in Practice (Paperback)
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Paperback. Condition: new. Paperback. Build Data Systems That Scale-From ETL to Real-Time StreamingThe 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 stacksHow to build robust ETL and ELT pipelinesReal-time stream processing with tools like Apache Kafka and Spark StreamingOrchestrating workflows using Apache AirflowWorking with structured and unstructured data at scaleData lake vs. data warehouse: when to use whatScaling pipelines with cloud-native tools (AWS, GCP, Azure)Ensuring data quality, observability, and monitoringBest practices for automation, versioning, and reproducibilityWhether 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798290199948
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